Dewan Redaksi. Contributors wanted! Pemimpin Redaksi Arief Yudhanto (Tokyo Metropolitan University)

Dewan Redaksi Pemimpin Redaksi Arief Yudhanto (Tokyo Metropolitan University)

Departemen/Staf Redaksi Artikel Utama: Muhammad Ery Wijaya (Kyoto University), Artikel Riset: Nirmala Hailinawati (Tokyo Institute of Technology), Berita dan Opini: Pandji Prawisudha (Tokyo Institute of Technology), Kilas Riset: Cahyo Budiman (Okinawa Institute of Science and Technology/ Institut Pertanian Bogor) Living: Retno Ninggalih (Sendai, Tohoku), Resensi Buku dan Film: M. Ridlo E. Nasution (Tokyo Metropolitan University), Desain Grafis dan Foto: Banung Grahita (Tokyo Metropolitan University/Institut Teknologi Bandung), Wawancara: Gerald Ensang Timuda (Lembaga Ilmu Pengetahuan Indonesia), Admin Situs: Bayu Indrawan (Tokyo Institute of Technology), Pandji Prawisudha (Tokyo Institute of Technology) Situs: io.ppijepang.org E-mail: [email protected]

Kontributor Foto sampul: Anzilni Fathia Amasya

Contributors wanted! Majalah Inovasi bertransformasi menjadi majalah populer untuk mengakomodasi pembaca dan contributor yang lebih luas. Kami menunggu tulisan dan foto anda! 1) 2) 3) 4) 5) 6) 7) 8) 9)

Foto esai di Jepang dan dunia (maksimum dua foto, dan esai sangat pendek 100 kata) Artikel populer (masuk ke dalam laporan utama; jumlah kata untuk artikel ini dibatasi 600 - 2000 kata; bahasa sederhana yang dapat dipahami siswa SMA; catatan kaki dan referensi maksimum 10 buah) Artikel riset yang bersifat semi popular (isinya serius, sekelas paper; catatan kaki dan referensi tidak lebih dari 20; jumlah kata untuk artikel ini dibatasi 2000-4000 kata) Berita tentang kerjasama Jepang-Indonesia; tentang kunjungan resmi kontingen Indonesia ke Jepang; berita tentang prestasi warga Indonesia di Jepang (200-300 kata, plus foto jika ada) Opini tentang isu-isu terhangat di Jepang dan Indonesia, tentang Indonesia, tentang Jepang, dan lain-lain (200300 kata, plus foto jika ada). Misal: opini tentang kebijakan karangan ilmiah yang dikeluarkan DIKTI. Kilas riset. Silakan mengirimkan abstrak dari artikel yang telah anda publikasikan di jurnal ilmiah atau proceeding. Sertakan satu gambar representatif. Resensi buku dan film. Setelah membaca buku baru atau menonton film, silakan berbagi di rubrik ini. Foto sampul buku dan ulasan pendek 100 – 300 kata. Wawancara. Anda dapat mengirimkan hasil wawancara informal anda dengan tokoh dari Indonesia tentang Jepang, sains, teknologi, seni dan lainnya. Living. Jika anda punya tips hidup hemat, belanja murah, tips sukses berkuliah dan bekerja di Jepang di sinilah tempatnya! Berbagilah dalam 300 kata.

Kirimkan tulisan atau foto ke email berikut: [email protected]

Isi

Isi

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Editori@l

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Topik Utama

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Profil Kebijakan Energi Pasca Tragedi Fukushima Sidik Permana

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Meninjau Kembali PLTN dan Kebijakan Energi di Jepang Muhammad Kunta Biddinika, Muhammad Aziz

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Artikel Lingkungan Municipal Solid Waste Treatment Using Hydrothermal Process to Produce a Renewable Energy Pandji Prawisudha, Srikandi Novianti

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Artikel Riset: Artikel Pemenang TICA 2011 (Cluster II) Development of Indonesian Automated Document Reader: Evaluation of Text Segmentation Algorithms Teresa Vania Tjahja, Anto Satriyo Nugroho

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Optimized Turbo Code VLSI Architecture for LTE using System Level Modeling and Assertion Based Verification Ardimas Andi Purwita, Trio Adiono

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Evaluation of Fingerprint Orientation Field Correction Methods A. A. K. Surya, A. S. Nugroho

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Algorithm and Architecture Design of Soft Decision Demapper for SISO DVB-T by Using Quadrant K-Best Probo Aditya N.I., Trio Adiono

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Application of Adaptive Neuro Fuzzy Inference System (ANFIS) for Lung Cancer Detection Software Sungging Haryo W., Sylvia Ayu P., M. Yusuf Santoso, Syamsul Arifin

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EDITORIAL

Tsunami menghempas perumahan di Natori, Prefektur Miyagi. (Foto: Associated Press)

Dewan redaksi menyampaikan belasungkawa sedalam-dalamnya kepada keluarga korban gempa dan tsunami yang terjadi di Tohoku pada 11 Maret 2011. Kami berharap segenap warga dan keluarga yang terkena musibah terus diberi kekuatan dan kesabaran.

*** Majalah INOVASI kali ini mengetengahkan topik utama ‘Satu Tahun Pasca Tribencana Tohoku‘. Tribencana (gempa-tsunami-ledakan reaktor nuklir) diawali dengan gempa yang berpusat sejauh 130 kilometer dari Sendai, Prefektur Miyagi. Gempa berkekuatan magnitudo 9 (tingkat guncangan 7 shindo) ini terjadi pada 11 Maret 2011, pukul 14.46. Gempa ini merupakan gempa terbesar dalam sejarah Jepang. Bumi pun terganggu: porosnya bergeser 10 hingga 25 cm sehingga lamanya siang dan orbit bumi sedikit berubah. Sistem peringatan tsunami Jepang dengan sigap bekerja. Sekitar seribu sensor menerima sinyal bahwa tsunami besar akan terjadi. Japan Meteorological Agency segera meneruskan sinyal ini, dan semua penduduk diharuskan segera menjauhi pantai. Sayangnya, tidak semua orang selamat dari bencana tsunami sore itu. Tsunami itu merenggut 15.854 nyawa. Belum lagi 3.203 orang dinyatakan hilang, dan 26.992

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orang mengalami luka-luka. Sekitar satu juta rumah hancur, dan lebih dari 500 ribu orang kehilangan tempat tinggal. Empat ribu jalan raya, 78 jembatan dan 29 rel kereta mengalami kerusakan. Limbah dan sampah yang berceceran ditaksir mencapai 25 ton. Tidak hanya itu, tsunami juga melumpuhkan fasilitas pembangkit listrik tenaga nuklir (PLTN), khususnya yang berlokasi di pantai timur, yaitu PLTN Fukushima Daiichi. Reaktor nuklir secara otomatis berhenti beroperasi (shut down) ketika diguncang gempa besar. Reaktor yang masih panas dengan cepat didinginkan oleh sistem pendingin. Sayangnya, sistem pendingin reaktor di PLTN milik TEPCO itu rusak dihempas tsunami, sehingga gagal menurunkan temperatur reaktor. Reaktor pun meleleh dan letupan terjadi. Radiasi nuklir pun terdeteksi di lingkungan sekitar reaktor. Penduduk panik, dan pemerintah segera bertindak. Mereka yang tinggal di radius 30 kilometer dari reaktor harus menyingkir sejauh-jauhnya.

EDITORIAL

Dampak dari kompleksitas tiga bencana gempa, tsunami dan nuklir ini sangat besar, tidak hanya dari sisi korban jiwa dan materi, namun juga dari sisi kemajuan teknologi. Jepang menon-aktifkan satu per satu reaktor nuklirnya yang berjumlah lebih dari 50. Reaktor itu harus menjalani pemeriksaan keselamatan. Reaktor terakhir yang dipadamkan adalah Unit 3 PLTN Tomari di Hokkaido. Pada tanggal 5 Mei 2012 Jepang dapat dikatakan ‘bebas‘ tenaga nuklir. Efek tragedi reaktor nuklir ini tidak hanya dialami Jepang. Segera setelah bencana tersebut, sejumlah negara mengkaji ulang atau membatalkan program pembangkitan nuklir mereka. Secara keseluruhan, tribencana ini membuat Jepang mengalami kerugian sekitar 16-25 triliun yen. Jepang diselimuti duka yang mendalam. Satu tahun berlalu, penduduk Fukushima, Miyagi dan Iwate terus melakukan perbaikan kawasan. Dua ratus kilometer dari sana, tepatnya di kawasan Chiyodaku, Tokyo, seribu orang lebih memenuhi Teater Negara. Perdana Menteri Yoshihiko Noda dan Raja Jepang Akihito melakukan doa bersama. Tepat pukul 14:46 keadaan menjadi senyap. Jepang mengheningkan cipta selama 60 detik. Di balik peringatan itu, sejumlah pertanyaan tersisa. Bagaimana masa depan penggunaan dan pengembangan energi nuklir di Jepang? Masihkah 30% energinya tetap bersumber dari nuklir? Karena itu, sebagai media informasi ilmiah yang berbasis di Jepang, majalah INOVASI edisi kali ini menampilkan sejumlah artikel mengenai pengembangan nuklir Jepang setelah tribencana. INOVASI memuat tulisan Dr. Sidik Permana, peneliti Institute for Science and Technology Studies yang saat bencana sedang berada di Prefektur Ibaraki yang bersebelahan dengan Fukushima. Beliau memaparkan tentang kebijakan nuklir Jepang pascatragedi Fukushima dalam tulisan berjudul “Profil dan Kebijakan Nuklir Pasca Tragedi Fukushima”. INOVASI juga mengundang Muhammad Kunta Biddinika (Crisis Center, Kedutaan Besar Republik Indonesia Tokyo) dan pakar kebijakan energi Dr. Muhammad Aziz untuk memberikan perspektif. Tulisan mereka yang berjudul “Meninjau Kembali PLTN dan Kebijakan Energi di Jepang“ memberikan data-data tentang PLTN, kebijakan energi dan pandangan warga Jepang tentang nuklir. Dua tulisan utama ini diharapkan dapat memberikan

pencerahan tentang kebijakan nuklir Jepang kepada para pembaca. Masih terkait dengan energi, INOVASI juga mengetengahkan tulisan tentang energi terbarukan yang dibangkitkan dari sampah kota. Sampah yang biasanya diabaikan karena polutif ternyata dapat diolah secara efisien dan self-sustain dengan metode hidrotermal yang diusulkan Dr. Pandji Prawisudha dan Srikandi Novianti. Artikel berjudul “Municipal Solid Waste Treatment Using Hydrothermal Process to Produce a Renewable Energy Source” menjabarkan teknologi konversi sampah kota menjadi bahan bakar setara batubara dan perbandingannya dengan teknologi pengolahan sampah lain. Selain itu INOVASI juga masih mengetengahkan artikel ilmiah pemenang dan finalis Tokyo Tech Indonesian Commitment Award (TICA) 2011 yang merupakan hasil riset mahasiswa/i di Indonesia. Nomor sebelumnya (Vol 19 No 3 Desember 2011) telah memuat Cluster-1 yaitu Business, Social Science and Urban Planning. Dalam nomor ini (Vol 20 No 1 April 2012) kami memuat lima artikel (tiga pemenang dan dua finalis) dari Cluster-2 yaitu Electronic-Electrical and Information Technology. Anda dapat menantikan artikel dari Cluster-3 (Applied Science and Technology) dalam nomor mendatang. Tak ada gading yang tak retak. Tak ada karya manusia yang sempurna. Redaksi INOVASI mengharapkan komentar, saran dan informasi dari pembaca setia. Anda dapat mengirimkan tulisan ke alamat surel kami: editor. [email protected]. Dan, selamat membaca! Salam hangat, Editor

Vol 19 No.4

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Topik Utama

Profil dan Kebijakan Nuklir

Pasca Tragedi

Fukushima Sidik Permana

Bandung Institute of Technology, Nuclear Physics and Biophysics Research Division Institute for Science and Technology Studies Indonesian Nuclear Network E-mail: [email protected]

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Topik Utama

G

empa terbesar dalam sejarah Jepang yang terjadi pada hari Jumat tanggal 11 Maret 2011 tepat pukul

14.46 dengan kekuatan 9 magnitude disusul oleh gempa besar dan kecil susulan, dan tidak lama setelah gempa besar tersebut bencana tsunami terjadi. Peristiwa bencana alam tersebut diawali

dengan bencana gempa sebagai pemicunya, kemudian diikuti oleh naiknya gelombang laut sehingga terjadi

tsunami, dan pada akhirnya berefek pada hancurnya fasilitas Pembangkit Listrik Tenaga Nuklir (PLTN) khususnya fasilitas pendinginan reaktor di PLTN Fukushima Daiichi, yang mengakibatkan terjadinya peristiwa penyebaran radiasi nuklir. Berkaitan dengan energi nuklir, Jepang menyadari bahwa potensi sumber daya alam yang terbatas dan akibat peristiwa krisis minyak dunia tahun 70an, mencoba memulai kebijakan diversifikasi energi serta keamanan dan kemandirian energi dengan penggunaan energi nuklir dan bahan bakar gas dalam rangka mengurangi kebergantungan terhadap minyak akibat pengurangan konsumsi batubara. Pilihan kebijakan nuklir sendiri diikuti dengan penguatan infrastruktur industri, riset dan pengembangan serta pendidikan. Dari hasil proyek turnkey PLTN Amerika Serikat yang dibangun di Jepang, Jepang berhasil mempelajari dan mengembangkan sendiri teknologi nuklirnya dengan menaikkan tingkat keamanan PLTNnya menyesuaikan dengan tuntutan

lingkungan alam sekitar, baik teknologi bangunan tahan gempa maupun penahan tsunami. Level keselamatan standar PLTN pun ditingkatkan sehingga model reaktor generasi selanjutnya, terutama Generasi ke-3, telah mengalami kemajuan yang cukup signifikan dari segi keselamatan. PLTN Fukushima Daiichi merupakan kompleks reaktor Jepang yang cukup tua, di mana Unit 1 sudah 40 tahun beroperasi dan beberapa lainnya mendekati umur yang sama. Generasi reaktor Fukushima ini termasuk Generasi ke-2 yang tentunya sudah memenuhi segi keselamatan reaktor baik saat beroperasi maupun pada saat darurat. Peristiwa kejadian bencana gempa dan tsunami ini memberikan sebuah pelajaran berharga, bahwa hilangnya semua sumber listrik

darurat reaktor (blackout station) menjadi peristiwa yang fatal yang berakibat pada kegagalan pendinginan sisa panas akibat peluruhan bahan bakar dan produk fisi yang ada di reaktor. Krisis nuklir Fukushima ini berbeda dengan krisis Three Mile Island (TMI) yang terjadi di Amerika Serikat pada tahun 1978 dan juga berbeda dengan peristiwa Chernobyl baik dari segi proses, pemacu dan radiasi yang dipancarkan, meskipun dari segi level International Nuclear and Radiological Event Scale (INES) mempunyai tingkat yang sama.1

Status PLTN di Jepang Sebelum dan Pasca Tragedi Fukushima Bencana gempa bumi dan tsunami pada 11 Maret 2011 memaksa

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Topik Utama

Status of the Nuclear Power Plants after the Earthquake

Gambar 1. Status PLTN di Jepang setelah terjadinya bencana gempa dan tsunami 5

PLTN berhenti beroperasi secara otomatis. Hal yang sama juga terjadi pada pembangkit listrik lainnya di saat bersamaan, di antaranya di Pembangkit Listrik Tenaga Air (PLTA) (20 unit) dan pembangkit listrik bahan bakar fosil (15 unit). Bencana tersebut juga melumpuhkan jaringan dan fasilitas transmisi serta distribusi pasokan listrik ke pelanggan dari pembangkit di Jepang bagian Timur dan Utara pulau Honshu. 2 Sebelum terjadinya bencana gempa dan tsunami, Jepang mengoperasikan sekitar 54 unit PLTN atau sekitar 31% pasokan listrik nasional. 3 Akibat dari bencana

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alam tersebut, sekitar 14 unit PLTN terkena dampak gempa dan tsunami; 3 unit di PLTN Onagawa, 4 unit di Fukushima Dai-ni, 1 unit di Tokai Dai-ni, dan 6 unit di Fukushima Dai-ichi (lihat Gambar 1). Sementara sisanya sekitar 40 unit reaktor tetap beroperasi karena tidak terkena dampak langsung dari bencana alam tersebut.4 Akibat tidak beroperasinya beberapa unit PLTN tersebut dan beberapa unit PLTA serta pembangkit berbahan bakar fosil, menyebabkan turunnya pasokan listrik hingga 20% dari total produksi listrik Tokyo Electric Power Company (TEPCO) dan Tohoku Electric Power Company (Tohoku

Topik Utama

Current Status of the Nuclear Power Plants in Japan (as of March 26, 2012)

Gambar 2 . Status PLTN di Jepang terbaru setelah terjadinya bencana gempa dan tsunami 6

Electric), atau setara dengan 10 – 15 Gigawatt milik TEPCO dan 2 – 3 Gigawatt milik Tohoku Electric.3 Seperti terlihat pada Gambar 2 mengenai status terbaru PLTN di Jepang pada akhir bulan Maret 2012, hanya satu unit PLTN yang beroperasi yaitu Unit 3 PLTN Tomari di Hokkaido yang nantinya juga akan dinonaktifkan (shutdown) karena memasuki masa pengecekan berkala bulan Mei 2012. Sementara 17 unit tidak beroperasi terdiri dari 14 unit yang terkena dampak langsung gempa dan tsunami , dan 3 unit di PLTN Hamaoka dinonaktifkan atas permintaan langsung pemerintah. Sebanyak 36 unit PLTN lainnya juga

tidak beroperasi dengan alasan telah memasuki masa pengecekan berkala dan lainnya ( JAIF, 2012).6 Kebijakan pemerintah Jepang untuk melakukan uji ketahanan (stress test) pada semua PLTN yang ada, memaksa hampir semua unit dinonaktifkan secara bersamaan atau saat mendekati pengecekan berkala masing-masing unit. Semua pengelola (operator) PLTN diwajibkan mengajukan aplikasi pelaksanaan uji ketahanan kepada badan pengawas (regulator) PLTN agar konfirmasi nilai keselamatan dapat dipenuhi berdasarkan tambahan prosedur yang perlu diikuti menyusul tragedi Fukushima.6 Dari 18 unit PLTN

yang baru-baru ini mengajukan pengecekan uji ketahanan, dua unit diantaranya sudah mendapat verifikasi dari badan pengawas dan telah diterima oleh pemerintah pusat untuk bisa kembali dioperasikan guna diajukan ke pemerintah daerah untuk mendapatkan penerimaan dari masyarakat terkait dioperasikan kembali PLTN tersebut, setelah menjalani serangkaian pemeriksaan dan uji keselamatan reaktor. 7 Dampak yang paling terasa dengan tidak dioperasikannya PLTN adalah pengurangan pasokan listrik bagi industri, selain tentunya pengurangan pasokan kepada sektor rumah tangga dan komersial. Pada

Vol 19 No.4

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Topik Utama

sektor industri, banyak industri yang berhenti beroperasi akibat tidak adanya pasokan listrik. Asosiasi Pengusaha Baja Jepang dan Pelaku Industri dan Utilitas Kelistrikan Jepang telah meminta pemerintah jepang untuk segera mengoperasikan kembali reaktor nuklir yang sudah menjalani serangkaian uji keselamatan untuk memenuhi kebutuhan listrik industri mereka, dan asosiasi tersebut kembali meminta pemerintah untuk segera menambah kapasitas nuklir Jepang untuk memenuhi kebutuhan industri ke depan.8 Sementara masyarakat di kalangan rumah tangga dan komersial juga dituntut untuk melakukan pengurangan pemakaian listrik dan melakukan efisiensi. Perusahaan dan industri di Jepang terpaksa menutup sebagian pabriknya dan membeli beberapa komponen produksi dari luar yang pada akhirnya meningkatkan harga

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produk mereka. Di sisi lain, karena pasokan energi tidak ada, pembangkit berbahan bakar fosil yang sudah tua dioperasikan kembali dan impor gas juga ditingkatkan untuk memenuhi kebutuhan energi dalam negeri Jepang. Dari sisi produsen listrik, karena biaya produksi listrik yang meningkat, secara perlahan produsen listrik seperti TEPCO telah menaikkan harga listrik sekitar 17% dalam rangka menyesuaikan biaya produksi tersebut, yang tentunya membuat konsumen harus menambah pengeluaran bagi pemakaian listrik. Di sisi lain meningkatnya konsumsi bahan bakar fosil mengakibatkan emisi gas CO2 mengalami kenaikan, yang berakibat pada target pencapaian Protokol Kyoto untuk pengurangan emisi gas CO2 terhambat dan mengalami kemunduran. Selain itu, muncul permasalahan yang disebut

”genpatsu money” yaitu uang PLTN bagi penduduk dan pemerintah lokal di tingkat kelurahan atau desa, berupa subsidi atau dana tanggungan bagi penduduk atau pemerintah daerah yang berada di lokasi fasilitas nuklir (salah satunya PLTN). Uang tersebut sebelumnya diberikan kepada masyarakat untuk membangun fasilitas daerah dan digunakan untuk meningkatkan kesejahteraan masyarakat yang tentunya menjadi pendapatan asli daerah. Dampak lain juga muncul ketika industri nuklir tidak beroperasi, yakni industri turunan atau industri terkait nuklir yang telah menyerap tenaga kerja lokal akan ikut tidak beroperasi. Hal ini berdampak langsung pada mata pencaharian penduduk setempat yang tentunya telah puluhan tahun merasakan manfaat dan kesejahteraan dengan adanya fasilitas PLTN (Liputan NHK Maret 2012, Setahun Fukushima).

Program Nuklir Jepang Pasca Fukushima Seperti diinformasikan pada bagian sebelumnya, Jepang saat ini telah menerapkan standar pengecekan keselamatan tambahan untuk memastikan bahwa semua unit PLTN bisa dijamin keselamatannya terutama terhadap gempa dan tsunami, salah satunya dengan menerapkan uji ketahanan. Pasca Tragedi Fukushima, pemerintah Jepang saat ini sedang berupaya menyusun kebijakan baru dalam rangka merevisi kebijakan jangka panjang energi nasional mereka. Salah satu poin penting dalam kebijakan itu adalah berupaya menyusun kebijakan yang tidak lagi bergantung

Topik Utama

kepada energi nuklir sebagai salah satu pilar penting pembangunan dan kemajuan Jepang yang telah dirintis sejak tahun 1960-an hingga saat ini. Kebijakan jangka pendek dan menengah Jepang diprediksi masih menggunakan energi nuklir dengan beberapa opsi, di antaranya tetap pada porsi yang ada saat sebelum tragedi Fukushima dengan meningkatkan level keselamatannya terutama dari tsunami, dan opsi menurunkan porsi nuklir secara bertahap. Dalam program jangka panjang untuk mengurangi kebergantungan terhadap nuklir, Jepang mencari formula terbaik untuk pasokan energinya dengan meningkatkan porsi energi terbarukan. Kebijakan ini dapat merevisi kebijakan terdahulu di mana Jepang akan menaikkan kontribusi energi nuklir di dalam energy mix mereka dari angka 30% (sebelum Tragedi Fukushima) menjadi 50% pada 2050. Kaji ulang kebijakan energi Jepang ini masih dalam tahap penyusunan dan masih akan ada perubahan bergantung kondisi dan hasil penyelidikan tim pakar yang ada. Program jangka pendek dan menengah adalah program dekontaminasi radiasi di sekitar PLTN dan khususnya di daerah evakuasi sehingga penduduk bisa kembali ke daerahnya semula, tentu setelah reaktor stabil dan tingkat radiasi turun hingga tingkat aman. Tentunya semua program di atas beriringan dengan program pembangunan kembali daerah-daerah yang terkena gempa dan tsunami, yang luasan daerah dan besaran kerusakannya lebih daripada bencana akibat kebocoran nuklir. Dalam satu tahun ini semua unit reaktor 1-3 Fukushima Daiichi telah mencapai

kestabilan dan diupayakan untuk segera memasuki tahap selanjutnya, yaitu mengurangi tingkat radiasi yang keluar dan mengeluarkan serta memindahkan bagian-bagian reaktor untuk segera di-dekomisioning. Dengan hanya mengoperasikan satu unit reaktor saja dari 54 unit PLTN, Jepang akan mengalami defisit energi khususnya saat menghadapi musim panas yang akan datang yaitu sekitar 20%. Satu unit tersebut dimiliki oleh Hokkaido Electric Power Company yang dalam periode kedepan akan memasuki pemeriksaan periodik dengan dinonaktifkan pada 5 Mei mendatang. Informasi terbaru dari Kabinet Perdana Menteri Yoshihiko Noda mengeluarkan pernyataan bahwa dua unit PLTN yang tidak beroperasi karena dinonaktifkan untuk keperluan pengecekan keselamatan milik Kansai Electric Power Company telah selesai dan siap memasok kembali energi bagi keperluan musim panas ini dalam keadaan aman. 7 Unit 3 dan Unit 4 PLTN Ohi di Prefektur Fukui di Jepang Barat merupakan unit reaktor pertama yang mungkin dapat dioperasikan kembali setelah berhenti beroperasi karena permintaan khusus atau pengecekan berkala reaktor di seluruh Jepang. Keputusan terbaru tersebut disambut hangat oleh pemimpin bisnis di wilayah Jepang Barat. Shigetaka Sato yang merupakan Ketua Kamar Dagang dan Industri Osaka mengatakan bahwa hal ini merupakan langkah besar bagi pengoperasian kembali unit-unit PLTN di Jepang. Menurut Sato, bagi Jepang, energi nuklir adalah kunci bagi keamanan pasokan energi di Jepang. Sementara itu, kepala Pemerintah Daerah Kyoto Keiji Yamada dan Pemerintah Daerah Shiga Yukiko Kada masih enggan dengan keputusan

pengoperasian kembali tersebut.7 Perdana menteri Noda menegaskan kembali bahwa keputusan terbaru bukan berarti bahwa kebijakan pemerintah untuk mengurangi kebergantungan energi nasional pada energi nuklir di masa datang telah berubah. Edano Noda mengatakan jika pemerintah tidak berhasil mendapatkan pengertian dari masyarakat untuk mengoperasikan kembali reaktor Ohi, maka masyarakat dan perusahaan daerah area listrik Kansai akan diminta untuk mengurangi penggunaan listrik sekitar 20% atau lebih pada musim panas mendatang. 7

Program Nuklir Dunia Pasca Tragedi Fukushima Sampai 5 Desember 2011 atau 8 bulan setelah tragedi Fukushima, sebanyak 434 unit PLTN di sekitar 30 negara di dunia masih beroperasi dengan total kapasitas terpasang 367,7 Gigawatt. Sekitar 64 unit PLTN saat ini sedang dalam proses pembangunan dengan mayoritas dibangun di Asia dan sekitar enam unit PLTN telah tersambung ke jaringan listrik (on-grid) pada tahun 2011, seperti PLTN Kaiga-4 di India, PLTN Lingao-4 dan CEFR di Cina, Chashma-2 di Pakistan, Bushehr-1 di Iran dan Kalini-4 di Rusia.9 Selama tahun 2010-2011, sekitar 15 unit reaktor di Asia dan tiga unit reaktor dibelahan bumi lainnya telah memulai proses pembangunan dan 11 unit telah tersambung ke jaringan listrik. Pascatragedi Fukushima, dalam bulan September 2011, Iran telah mengizinkan PLTN pertamanya

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beroperasi. Beberapa negara lain seperti UEA dan Turki juga telah memesan PLTN unit pertamanya ke Korea dan Rusia, dan negaranegara tersebut terus melanjutkan program nuklirnya. Belarus sebagai salah satu negara yang daerahnya terkontaminasi radiasi dari tragedi Chernobyl, menandatangani kesepakatan antarpemerintah pada bulan Oktober 2011 untuk PLTN pertama mereka, sebagaimana Bangladesh juga melakukan penandatanganan kerja sama yang serupa. Di ASEAN, Vietnam telah menandatangani kontrak pinjaman dana untuk pembangunan PLTN pertamanya pada bulan Desember 2011.9 Sementara itu di Eropa, Inggris telah menandatangani kontrak dengan Prancis untuk pembangunan satu unit PLTN baru yang siap menyerap tenaga kerja 350 orang.10 Masih di tahun yang sama, organisasi dunia untuk energi nuklir atau International Atomic Energy Agency (IAEA) telah mencatat dan mengklasifikasikan berbagai program nuklir di beberapa negara terutama pasca Fukushima. Tiga negara telah memesan unit PLTN baru yaitu Belarusia, Turki dan Uni Emirat Arab (UEA), dan enam negara lain telah memutuskan serta memulai persiapan infrastruktur dalam rangka pembangunan PLTN mereka, di antaranya Bangladesh, Yordania, Vietnam, Polandia, Mesir dan Nigeria. Sementara tujuh negara lainnya telah dengan aktif mempersiapkan program nuklir mereka untuk pembangunan PLTN di masing-masing negara namun belum mempunyai keputusan yang final terhadap keputusan kapan membangun PLTN pertama mereka,

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di antaranya Chili, Indonesia, Malaysia, Maroko, Thailand dan Arab Saudi. Sebanyak 15 negara, di antaranya juga di benua Asia dan Afrika, mempertimbangkan dan berniat mengembangkan program nuklir. Sementara delapan negara lainnya tidak mempunyai rencana membangun PLTN di dalam negeri, namun telah menginformasikan ketertarikannya dalam program nuklir di masing-masing negara tersebut. Sekitar empat negara pascatragedi Fukushima telah membatalkan program nuklir mereka, di antaranya Kuwait, Italia, Venezuela dan Senegal. Sementara itu, di saat bersamaan, IAEA juga telah melakukan sebuah kaji ulang terhadap infrastruktur energi nuklir terintegrasi bagi beberapa negara untuk melihat dan meninjau status negara-negara tersebut dalam mempersiapkan program nuklir mereka khususnya bagi negara yang telah bersedia dan tertarik untuk membangun unit PLTN di negara mereka seperti pada tahun 2009 di Yordania, Indonesia dan Vietnam, tahun 2010 di Thailand dan 2011 di UEA dan Bangladesh serta kaji ulang lanjutan bagi Yordania, dan tahun 2012 juga dilakukan kaji ulang untuk Polandia dan Belarusia,untuk beberapa misi tambahan. 10 Menanggapi kejadian Tragedi Fukushima, beberapa negara yang mempunyai program nuklir dan mengoperasikan serta mengembangkan PLTN di negara mereka telah memberikan tanggapan yang cepat, seperti Prancis segera membuat penilaian (assesment) keselamatan fasilitas nuklir khususnya PLTN, yang telah dimulai 23 Maret 2011 sebagai bagian dari

perintah perdana menteri Perancis untuk meng-audit instalasi-instalasi nuklir negara tersebut. Sementara itu Korea juga melakukan tindakan yang sama dalam menanggapi Tragedi Fukushima dengan melakukan inspeksi khusus ke fasilitas-fasilitas nuklirnya, termasuk memasukkan 50 rencana jangka pendek dan panjang negeri tersebut dalam program nuklir mereka.11 Dalam estimasi dan proyeksi IAEA, diprediksikan bahwa pasca Fukushima ini, program nuklir dunia akan mengalami kemajuan yang lambat atau bahkan mengalami keterlambatan, namun kejadian tersebut tidak menurunkan pertumbuhan dan kenaikan program energi nuklir dunia. Fakta yang menarik adalah, beberapa negara pendatang baru dalam energi nuklir lebih tertarik untuk mengembangkannya dan minat negara-negara tersebut masih tinggi.9

Topik Utama

Referensi 1. INES (the International Nuclear and Radiological Event Scale) Rating on the Events in Fukushima Dai-ichi Nuclear Power Station by the Tohoku District - off the Pacific Ocean Earthquake. Ministry of Economy, Trade and Industry. News relese, April 12, 2011. URL: http://www.nisa.meti.go.jp/english/files/en20110412-4.pdf, diakses 5 Mei 2011. 2. Over 30% of listed firms report damage, NHK Report, 25 Maret 2011. 3. Impact to TEPCO's Facilities due to Miyagiken-Oki Earthquake. Tokyo Electric Power Company (TEPCO) Press Release (Mar 11, 2011). URL: http://www.tepco.co.jp/en/press/corp-com/release/11031105-e.html, diakses 2 Mei 2011. 4. Sidik Permana, Krisis Nuklir Fukushima Dai-ichi, Vol. 19 | XXII | Juli 2011, INOVASI online. 5. Information on status of nuclear power plants in Fukushima. Japan Atomic Industrial Forum. April 13, 2011. URL: http://www.jaif.or.jp/english/news_images/pdf/ ENGNEWS01_1302693266P.pdf, diakses 4 Mei 2011. 6. http://www.jaif.or.jp/english/news_images/pdf/ ENGNEWS02_1333964448P.pdf (diakses 14 April 2012) 7. http://mainichi.jp/english/english/newsselect/ news/20120414p2g00m0dm004000c.html (diakses 14 April 2012) 8. http://www.japan-press.co.jp/modules/news/index.php?id=2992 (diakses 16 April 2012) 9. IAEA, The World Nuclear Energy Prospects after Fukushima Accident : IAEA Projections. IAEA and Nuclear “New Comers”, IAEA Action Plan on Nuclear Safety, International Conference of GLOBAL 2011, December 12-15, 2011, Chiba, Japan. 10. http://uk.reuters.com/article/2012/02/20/uk-kier-idUKTRE81J0C220120220 (diakses 15 April 2012). 11. Soon Heung Chang, Perspective on Korean Nuclear Energy after the Fukushima Accident, International Conference of GLOBAL 2011, December 12-15, 2011, Chiba, Japan.

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Meninjau Kembali PLTN dan

Kebijakan Energi

di Jepang

Muhammad Kunta Biddinika*1 dan Muhammad Aziz2, 1

Dompet Dhuafa Foundation, Japan Operation Office Advanced Energy Systems for Sustainability, Tokyo Institute of Technology *Email: [email protected] 2

B

erbagai kalangan menunggu apa langkah jangka menengah dan panjang yang akan diambil oleh Jepang pasca-kecelakaan Pembangkit Listrik Tenaga Nuklir (PLTN) Fukushima Dai-ichi. Di tengah berbagai tekanan publik dalam negerinya, Jepang disinyalir akan mengikuti langkah Jerman untuk segera menghentikan program nuklir sebagai pembangkit listrik.

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Tulisan ini ingin menyuguhkan gambaran nyata di Jepang yang bersumber dari data-data nyata dan valid serta menjauhkan dari sumbersumber yang tendensius dan kental nuansa politisnya. Dengan demikian, meluruskan pandangan terhadap apa yang sebenarnya terjadi di Jepang, termasuk kebijakan seperti apa yang realistis untuk menjadi pilihan terbaik bagi Jepang saat ini dan yang akan datang. Sebagai gambaran, pasokan energi primer untuk pembangkit listrik di Jepang hingga sebelum terjadi bencana tsunami yang lalu terdiri dari 29.3% nuklir, 29.3% LNG, 24.9% batu bara, 9.3% air, 7.1% minyak dan 1.1% energi baru. 1 PLTN menjadi penyangga utama beban dasar (baseload) di Jepang saat ini, mengingat skala dan kemampuannya yang bisa menghasilkan listrik dengan relatif stabil. Jepang merupakan negara yang miskin cadangan energi primer. Mau tidak mau, negara ini harus mengimpor sumber energi utamanya dari negara-negara lain, termasuk Indonesia. Sebagai gambaran, total konsumsi listrik di Jepang per tahun mencapai 7 kali lipat dari Indonesia, meski jumlah penduduknya lebih sedikit. 2

Pandangan Masyarakat Jepang Setelah Bencana Gempa yang disertai tsunami di daerah Jepang utara berdampak besar baik secara psikologis maupun ekonomis pada masyarakatnya. Dari hasil survey yang dilakukan oleh beberapa media di Jepang setelah terjadinya bencana, baik oleh koran (yakni: Tokyo Shinbun, Yomiuri

Shinbun, dan Asahi Shinbun) maupun stasiun televisi (yakni: TBS dan NHK), lebih dari 50 persen masyarakat Jepang masih setuju nuklir sebagai salah satu pilihan pembangkit listrik. Termasuk yang banyak diharapkan oleh masyarakat melalui survey tersebut adalah tetap meneruskan pengoperasian PLTN setelah adanya penguatan struktur bangunan maupun reaktor terhadap ancaman bencana gempa dan tsunami. Masyarakat Jepang meyadari benar negaranya sangat minim akan cadangan energi primer. Di sisi lain, mereka sangat membutuhkan energi listrik dalam jumlah besar untuk menjalankan perekonomiannya. Industri-industri berat yang menjadi kunci perekonomian Jepang pun sempat berhenti akibat kurangnya pasokan listrik. Hal ini berimbas ke perekonomian Jepang yang sempat berada pada taraf yang mengkhawatirkan pasca gempa dan tsunami. Dari hasil survey serta kenyataan di lapangan, bisa diketahui betapa tenang dan sadarnya orang Jepang dalam menentukan pilihan pasokan energi mereka meski setelah terjadinya kecelakaan PLTN Fukushima Dai-ichi. Memang persentase dukungan terhadap PLTN berkurang setelah terjadinya gempa, namun angka pengurangan itu belumlah membuat dukungan menjadi berbalik. Harga listrik di Jepang per kWh untuk tiap-tiap sumbernya adalah: nuklir 5.3 Yen, LPG 6.2 Yen, air 11.9 Yen, batubara 5.7 Yen, dan minyak 10.7 Yen. 3 Dari sini, bisa kita ketahui bahwa nuklir merupakan sumber energi termurah dibanding sumber energi lainnya. Begitu

juga emisi karbon dioksida (CO2) pembangkit dari nuklir sangatlah kecil, sekitar 22 g-CO2/kWh. Angka ini hampir sama dengan emisi dari Pembangkit Listrik Tenaga Air (PLTA) (18 g-CO2/kWh) dan lebih kecil dibanding Pembangkit Listrik Tenaga Surya (PLTS) yang besarnya adalah 59 g-CO2/kWh.4

Kebijakan Energi ke Depan Beberapa tahun ke depan, pola dasar pasokan energi utama untuk listrik diperkirakan tidak akan jauh dari pola dasar, yaitu 30% nuklir, 30% batubara, 20% gas dan minyak, dan 20% energi terbarukan. Meski belum diputuskan secara resmi, nuklir diperkirakan akan memberi pasokan sekitar 23-25%. Paling tidak itu akan menjadi pola dasar di Jepang tahun 2020-2030. Nuklir akan tetap menjadi salah satu pilihan untuk beban dasar pembangkitan listrik di Jepang pada beberapa dekade ke depan. Fokus utamanya adalah memperbaiki dan meningkatkan ketahanan bangunan dan reaktor terhadap gempa dan tsunami untuk beberapa tahun ke depan ini. Meski demikian, peningkatan keselamatan operasi reaktor sendiri juga tidak lepas dari perhatian. Untuk batubara, teknologi Integrated Gasification Combined Cycle (IGCC) yang mampu membangkitkan listrik dengan efisiensi hingga 50% akan diterapkan. Selain itu, teknologi lain untuk meningkatkan efisiensi Pembangkit Listrik Tenaga Batubara (PLTB) seperti Integrated Gasification Fuel Cell (IGFC), maupun teknologi

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penangkapan karbon (Carbon Capture Storage (CCS)) guna mengurangi emisi karbon juga sedang giat-giatnya dikembangkan. . Perubahan yang sangat signifikan terjadi di energi terbarukan, seperti air, panas bumi, biomassa, surya, dan angin, yang diprediksi akan naik hingga 20%. Hanya saja, teknologi untuk menjadikan energi terbarukan tersebut sebagai penopang beban dasar pembangkitan listrik di Jepang masih sangat jauh dari harapan. Hal ini berkaitan dengan skalanya yang kecil dan sifatnya yang fluktuatif sehingga mempengaruhi kualitas frekuensi dan voltase listrik yang dihasilkannya. Apalagi standar kualitas voltase di Jepang sangat ketat yaitu 94-106 Volt. Di antara beberapa sumber energi terbarukan tersebut, PLTS menjadi salah satu target utama sumber listrik dan panas di Jepang untuk beberapa tahun mendatang. Baik yang berskala kecil seperti Solar Home System (SHS) ataupun besar. Hambatan yang ditemui adalah biayanya yang mahal dan lahan yang dibutuhkan juga luas. Luas lahan ini yang sulit diperoleh untuk negara seperti Jepang. Selain itu, sifat fluktuatif tenaga surya baik karena cuaca dan juga perbedaan siang dan malam, membuatnya tergantung pada teknologi-teknologi penyimpan dan pengatur listrik seperti baterai, fuelcell dan lain-lain. Sayangnya, jumlah fasilitas dan teknologi penyimpanan listrik ini masih jauh dari harapan. Teknologi baru seperti Vehicle to Grid (V2G) yang memanfaatkan mobil listrik atau hibrida sebagai penyimpan sekaligus penyedia listrik juga masih dalam tahapan riset. Semua pertimbangan di atas

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membuat energi di Jepang hingga dua dekade mendatang masih didominasi oleh peran nuklir. Mau tidak mau, hal ini dikarenakan kondisi perekonomian Jepang sangat bergantung kepada kestabilan dan keamanan pasokan energi.

Referensi: 1.The Federation of Electric Power Companies of Japan, 2011, Electricity Review Japan 2011, http://www.fepc.or.jp/english/library/electricity_eview_japan/__icsFiles/ afieldfile/2011/01/28/ERJ2011_full.pdf, diakses 1 Mei 2012 2.Wikipedia, 2012, List of countries by electricity consumption, http://en.wikipedia.org/wiki/ List_of_countries_by_electricity_consumption#cite_note-CIA-0, diakses 1 Mei 2012 3. Matsuo, Y., Nagatomi, Y., Murakami, T., 2011, Thermal and Nuclear Power Generation Cost Estimates Using Corporate Financial Statements, http://eneken.ieej.or.jp/data/4103.pdf (diakses 1 Mei 2012) 4. World Nuclear Association, 2012, Comparative Carbon Dioxide Emissions from Power Generation http://www.world-nuclear.org/education/comparativeco2.html (diakses 1 Mei 2012)

Artikel Lingkungan

0XQLFLSDO 6ROLG :DVWH 7UHDWPHQW 8VLQJ +\GURWKHUPDO 3URFHVV WR 3URGXFH D 5HQHZDEOH (QHUJ\ 6RXUFH Pandji Prawisudha, Srikandi Novianti Dept. of Environmental Science and Technology, Tokyo Institute of Technology, Japan E-mail: [email protected]

Abstract An alternative treatment by converting Municipal Solid Waste (MSW) to alternative fuel by employing hydrothermal treatment in 1 ton/batch capacity commercial scale plant was conducted by applying medium pressure saturated steam at approximately 215 °C in a stirred reactor for 30 minutes. The process yielded uniform pulp-like product with four-fold increasing density to reach 75% waste volume reduction. The product also showed an average heating value of 18 MJ/kg, which is similar to that of low-grade sub-bituminous coal. The energy balance calculation revealed that the required energy for the hydrothermal treatment was as low as one-ninth of the energy content in the product, which indicates that the hydrothermal treatment is a self-sustain system and requiring lower energy than conventional waste-to-fuel treatment processes. It can be concluded that the hydrothermal treatment would be a viable way to treat and convert the MSW to alternative renewable energy source. Key words: renewable energy, waste treatment, hydrothermal process, alternative fuel ©2011. Persatuan Pelajar Indonesia Jepang. All rights reserved.

1. Introduction Due to the increasing price of crude oil, many industries are now using coal; this has resulted in a significant increase in the demand and also in the price of coal, which increased by 150% in 2008.1 As a result, an alternative solid fuel is greatly needed to replace or partially substitute coal as the main fuel. On the other hand, municipal solid waste (MSW) has become a severe problem in many developing and developed countries due to the limited lifetime of final

waste disposal facilities. In Japan for example, the lack of final disposal facilities is still a major concern because the remaining lifetime of those in use is only 18 years.2 Current waste treatment technologies, however, are still not able to eliminate the waste while meeting three conditions: environmentally friendly, economically feasible, and high processing capacity. The aforementioned conditions should promote the usage of MSW as an alternative solid fuel. Treating the MSW can solve the problem of the large quantities of MSW, and

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thus the treated MSW can be supplied as solid fuel for the industries. Despite the advantages of MSW as a fuel, its very high moisture content and irregular form make it difficult to implement. To overcome these problems, an innovative hydrothermal treatment technology has been developed in Tokyo Institute of Technology for converting high moisture content solid wastes into dried uniform pulverized coal-like solid fuel using low energy consumption.3 The process differs from other processes such as super and sub-critical water oxidation, not only in the aspect of temperature but also in its water phase, which uses the saturated steam. This configuration will lead to lower capital cost to build the treatment plant. A commercial scale plant of the process, shown in Figure 1 implements a closed loop system principle. The 3 m3 reactor is capable of handling up to one ton of waste per batch, generally applying saturated steam while mixing the waste and steam using rotor blades driven by a 30 kW electric motor. At the beginning of the process, waste is fed into the reactor to undergo the hydrothermal reaction with

saturated steam supplied from the oil-fired boiler, while being stirred by a rotor unit to obtain homogeneous products. After reaching the target temperature, the reactor is set to maintain the temperature for a certain holding period. Finally, bleeding the pressurized steam to the condenser until the reactor reaches atmospheric pressure completes the hydrothermal treatment. The products can then be obtained by rotating the stirrer that serves as a screw conveyor. A total of three hours is required to complete the process. In this paper, performance of the hydrothermal treatment on MSW to produce a renewable energy source will be presented. Typical energy balance calculation of the treatment will also be described and compared with conventional waste treatment.

2. Experimental method 2.1. Hydrothermal treatment experiment The hydrothermal treatment experiments on MSW were conducted in Japan using the facility described above. Sampling of the raw MSW was based on the Japanese

Raw MSW

Fuel

Boiler

Steam Reactor Rotor Unit

Product Water

Water Treatment

Figure 1. Hydrothermal treatment plant diagram

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standard JIS K0060. Three process parameters, i.e., (1) the pressure and its related steam temperature, (2) the holding period, and (3) the amount of MSW, were presented in Table 1. The optimum experimental condition was chosen from the results of previous hydrothermal experiments.4 During the experiment, the reactor temperature, the water and fuel consumption, and the motor power consumption data were taken at certain intervals to obtain energy and mass balance and the operating characteristics of the hydrothermal treatment. The final solid products and the condensed water were then collected in sealed containers for analysis. Table 1. MSW Experimental

Parameter Pressure

Unit MPa

Value 2.0

Temperature Holding period MSW mass

°C min kg

215 30 705

2.2. MSW and product composition analysis Physical characteristics of the raw MSW and the hydrothermally treated products were analyzed based on the Japanese Ministry of Health and Welfare regulation no.95/1977. Moisture contents were obtained by drying the crushed samples in a constant-temperature oven at 105 °C, and the ash contents were obtained by inserting the samples into a constant-temperature oven maintained at 550 °C. The mass of the combustible content was obtained from the difference between mass of the dried samples and their ash weight.

2.3. Heating value analysis To confirm its feasibility as a fuel, the heating values of the raw MSW and the hydrothermally treated products were analyzed according to the Japanese standard JIS M8814. The samples were dried at 105 °C, crushed, and each sample were analyzed by using Shimadzu CA-4PJ bomb calorimeter.

3. Experiment results 3.1 Composition and heating value of raw MSW and product The raw MSW and product characteristics are shown in Table 2. Raw MSW and Product Characteristics

Parameter Density Moisture content Combustible content Ash content Higher Heating Value

Unit g/cm3 % % % MJ/kg

Raw MSW 0.15 33 50 17 18

Treated Product 0.61 44 46 10 18

Table 2. It can be seen that the density of hydrothermallytreated product was higher, approximately four times than that of the raw MSW in dry form. It can be predicted that 75% waste volume reduction by hydrothermal treatment can be achieved. The product exhibited higher moisture content, but almost equal heating value in the dry form compared to that of the raw MSW. The heating values of the raw MSW and the hydrothermally treated product were almost equal to that of low-grade sub-bituminous coal (approximately 19 MJ/kg5). Thus, it is possible to use the hydrothermallytreated products as a coal alternative solid fuel.

3.2 Product appearance The hydrothermal treatment process has produced grayish uniform slump products, as shown in Figure 2. It was reported that at higher reaction temperature and longer holding period, the hydrothermally-treated products became more uniform.4 The uniformity and visually smaller particle size can be explained by the fact that the pressure and temperature in the hydrothermal process would result in the particle breakage of MSW as shown in Figure 3.

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The energy balance equation can be derived as:

...(1)

while the “Energy from steam” can be obtained from:

...(2)

and the “Energy for steam injection phase” can be derived as:

...(3)

along with the “Energy for holding period”, which can be obtained from:

...(4)

Fuel

Water

Temperature

Motor Ampere

300

250

200

200 150 150 100 100 50

50 0

Temperature (oC) Motor Ampere (A)

Fuel Consumption (l) Water Consumption (l)

250

0 0

3

16

31

34

57

64

71

Time (min) Figure 4. Measured data from hydrothermal treatment plant

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Table 4. Comparison between Various Waste-to-Fuel Treatment System

Parameter

kg/m3 % MJ/kg

3 solid pulp 610 75 18

Anaerobic Digestion6,7 organic sorter crusher digester mixer 1-3 weeks gas 1.23 0 16-20

%

100

12-25

100

%

11.3

20-40

8-10

Unit

Raw MSW input

reactor steam generator

Equipment list Retention time Product type Product density Volume reduction Product’s calorific value Product/raw MSW Energy requirement per energy content in the product

Hydrothermal Treatment any

hour

5. Conclusions An innovative hydrothermal treatment for MSW has been developed, and a commercial-scale plant experiment has been conducted to obtain its performance for converting the MSW into alternative fuel. Uniform, pulp-like products were produced from the reactor. The calorific values of the hydrothermally-treated products were not substantially altered, almost equal to that of low-grade sub-bituminous coal. Energy balance calculation revealed that the required energy for the hydrothermal treatment was as low as one-ninth of the energy content in the product, which indicates that hydrothermal treatment is a selfsustain system and requiring lower energy input than other waste-to-fuel treatment processes. Considering its advantages, the hydrothermal treatment can be considered a viable way to treat and convert the MSW to alternative renewable energy source.

6. Future applications

RDF Pelletizing8 any sorter dryer crusher pelletizer continuous solid pellet 300-700 50-80 12-16

sludge9, as shown in Figure 6. This way the process can be applied to treat almost any kind of waste (unutilized resources), resulted in total recycling of material from human activities. Usable Products

Unutilized Resources - MSW - Agricultural Waste

- Sewage Sludge - Animal Manure

Solid Fuel

Hydrothermal Treatment

Liquid Fertilizer

Livestock Feed

- Food Residue - Organic Waste

Figure 6. Application of hydrothermal treatment

Added with the capability of the treatment to convert the organic chlorine in the plastic into inorganic, water-soluble chlorine that can be water washed10, the hydrothermal treatment can produce a clean fuel from plastic waste, which is a major problem of MSW usage as fuel.

The hydrothermal treatment is able to convert not only MSW to solid fuel but also from other unutilized resources to other usable products such as fertilizer from sewage

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References The Asahi Shimbun. Coal prices surging due to global demand, Australia flooding. March 4th 2008. Ministry of Environment Japan. State of discharge and treatment of municipal solid waste in FY 2008. 2010. Sato K, Jian Z, Soon JH et al. Studies on fuel conversion of high moisture content biomass using middle pressure steam. Proc. Thermal Eng. Conf.: G132. 2004. Prawisudha P. Conversion of Municipal Solid Waste into coal co-firing fuel by hydrothermal treatment. 2nd AUN/SEED-Net RCNRE: #G_005. 2010. American Society for Testing and Materials (1999) Gaseous Fuels: Coal and Coke; in Annual Book of ASTM Standards. Braber K. Anaerobic digestion of Municipal Solid Waste: A modern waste disposal option on the verge of breakthrough. Biomass & Bioenergy. 1995(9):365-376. Macias-Corral M, Samani Z, Hanson A et al. Anaerobic digestion of municipal solid waste and agricultural waste and the effect of co-digestion with dairy cow manure. Bioresource Technology. 2008(99): 8288-8293. Caputo AC, Pelagagge PM. RDF production plants I: Design and costs. Applied Thermal Engineering. 2002(22): 423-37. Jambaldorj G, Takahashi M, Yoshikawa K. Liquid fertilizer production from sewage sludge by hydrothermal treatment. Proc. Int’l Symp. on EcoTopia Science. 2007. Prawisudha P, Namioka T, Lu L et al. Dechlorination of simulated plastic waste in lower temperature employing hydrothermal process and alkali addition. J. of Environmental Science & Engineering. 2011 Vol. 5(4): 432-439.

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'HYHORSPHQW RI ,QGRQHVLDQ $XWRPDWHG 'RFXPHQW 5HDGHU (YDOXDWLRQ RI 7H[W 6HJPHQWDWLRQ $OJRULWKPV Teresa Vania Tjahja1, Anto Satriyo Nugroho2 1 Faculty of Information Technology/Swiss German University, 2 Center for Information and Communication Technology (PTIK)/ Agency for the Assessment and Application of Technology (BPPT) E-mail: [email protected], [email protected]

Abstract

I

ndonesian Automated Document Reader (I-ADR) is an assistive technology for people with visual impairment, which converts textual information on paper documents to speech. This research is conducted to develop a prototype of I-ADR featuring Optical Character Recognition (OCR), Text Summarization, and Text-to-Speech (TTS) Synthesizer modules. The main focus is the Text Segmentation module as an integral part of OCR. In this study, Text Segmentation algorithms for grayscale and color images are developed and evaluated. Text segmentation for grayscale images uses an improved version of Enhanced Constrained Run-Length Algorithm (CRLA)1, while segmentation for color images employs Multivalued Image Decomposition algorithm2 combined with the improved Enhanced CRLA. Based on the experiments, the success rate for grayscale images is 100% and for color images is 96.35%. Keywords: visual impairment, text segmentation, text summarization, text-to-speech synthesizer, OCR

1. Introduction As technology advances, more documents are converted to or available in their electronic forms. However, paper remains as the most common medium for carrying information, especially in developing countries such as Indonesia. Unfortunately, that kind of information is not available for people with visual impairment. To improve the accessibility of visually-impaired people to obtain textual information on papers, the Agency for the Assessment and Application of Technology develops Indonesian Automated Document Reader (I-ADR), which consists of

4 main modules as shown in Figure 1: voice-based user interface, Optical Character Recognition (OCR), Text Summarization, and Text-to-Speech (TTS) Synthesizer. This research is focused on Text Segmentation module as an integral part of the OCR module. The next sections of this paper are organized as follows: section 2 explains the proposed I-ADR system and section 3 provides experimental results and discussions, which will be concluded in section 4.

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2. Proposed System In this research, we have integrated three of the four main modules: OCR, Text Summarization, and TTS Synthesizer. In this section, each module will be discussed, emphasizing on Text Segmentation in OCR module.

Figure 1 Indonesian Automated Document Reader modules

2.1. Optical Character Recognition (OCR) The current I-ADR system accepts document images (both color and grayscale) as inputs, locates and extracts text from the image, and produces a speech based on the extracted text. The OCR module has a pre-processing submodule, which aims to remove noise with median filter and simplify grayscale image representation with binarization using Otsu’s thresholding scheme. The resulting binary image is then passed to Text Segmentation submodule. result

(a)

(b)

Figure 2 Result of Multivalued Image Decomposition algorithm: (a) Original image, (b) Bit-dropping and color quantization

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Figure 3 Multivalued Image Decomposition results

For color document images, the algorithms starts with Multivalued Image Decomposition algorithm2 for simplifying the color image representation. The algorithm has 3 main steps: bit-dropping, color quantization, and decomposition of the image based on colors. The result of the decomposition process (shown in Figure 2 and Figure 3) are several images, each of which containing objects of a particular color. Since an image from the decomposition process has only two colors, foreground and background, the decomposition results are then converted into binary grayscale images (with only two intensities). The next step is to extract text from each of those images. Text extraction from binary images is performed using Enhanced Constrained Run-Length Algorithm (CRLA)1. The algorithm basically groups objects in an image into homogeneous regions based on their size. For instance, characters in a text line may form a homogeneous region, while a picture forms another region. These regions will be examined further to be classified as text or non-text. For a more accurate result, the examination should be performed on each individual regions, which is not described particularly in the Enhanced CRLA paper.

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To obtain each regions, the general idea is to scan through the image and use histogram analysis to determine the starting and ending points of each individual regions. With histogram analysis, we basically count the number of foreground and/or background pixels in a particular area (row, column, etc.). When scanning through the image, a row with foreground pixels is considered as the starting row of a region, and the first row without foreground pixels after the starting row is considered as the ending row. For each range of rows (from starting to ending rows), the starting and ending columns of the regions are determined in the similar manner as the rows. Figure 4 illustrates the text segmentation problem encountered during our research. Consider Figure 4(a) as a part of document image. The grey area represents picture, while the black ones represents text. If we perform the commonly used raster scan (vertical and horizontal), the results are Figure 4(b) and (c), with the candidate individual region in Figure 4(c) still covers several text lines. To further separate those text lines, one more vertical scan should be performed for each candidate regions. The results are Figure 4(d) and (e), where the picture is

still maintained as one region, while the text lines can be separated. A more complex example is given in Figure 5. Figure 5(a) illustrates a part of a document image. Again, the gray areas represent pictures and the black ones text lines. If three scans as used for Figure 4(a) is performed for Figure 5(a), the results are shown in Figure 5(b) and (c), with the lower-left candidate region still consists of more than one individual region. In this case, we need two more scans: one horizontal scan to separate picture X

(a)

(b)

(c)

Figure 5 Illustration of more complex text segmentation problem

Y X

(a)

Figure 6 Candidate region in Figure 5(b) in larger view

(b)

(d)

(c)

(e)

Figure 4 Illustration of simple text segmentation problem

from picture Y and the text line, and another vertical scan to separate picture Y from the text line (see Figure 6). For the implementation in program code, the number of scans along with the scanning order needs to be determined. However, considering the two previous examples, the scans required for arbitrary layout may vary infinitely. Therefore, we propose that the scans are performed recursively and the recursion for a candidate region terminates when the region cannot be divided any further3. The pseudocode of the proposed recursive scans is shown in Figure 7. When a candidate individual region is found, it is classified as text or non-text by calculating its Mean Black Run-Length (MBRL) and Mean Transition

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tion2 and Enhanced CRLA1, equipped with our proposed recursive scans, and achieved 96.35% accuracy based on our experiments. Despite the relatively high success rate, there are still some issues left for future development, including the system’s evaluation with large-scale data and improvement of the algorithm to handle non-Manhattan layouts.

Reference Sun HM. Enhanced Constrained Run-Length Algorithm for Complex Document Layout Processing. International Journal of Applied Science and Engineering. Dec. 2006. 4(3): 297309. Jain AK, Yu B. Automatic Text Location in Images and Video Frames. In Proc. of 14th International Conference on Pattern Recognition, 1998, pp. 1497-1499. Brisbane, Qld.

Figure 8 Examples of text segmentation results with the proposed system

recognized characters. Often, the errors were caused by imperfect character shapes that were altered during normalization process, which involves scaling the segmented character image.

3.3. Post-processing Results The current post-processing module, which performs word correction and utilizes LCS algorithm, had 94.57% success rate (obtained by manual observation). The errors occurred in this module were caused by too many character recognition error in a word, and a word might be broken into several strings or several words combined into one due to space insertion errors.

Tjahja TV, et al. Recursive Text Segmentation for Indonesian Automated Document Reader for People with Visual Impairment. In Proc. of 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011), CD-ROM B2-2. Bandung, Indonesia. Jones NC, Pevzner PA. An Introduction to Bioinformatics Algorithm. Cambridge, Massachusetts: The MIT Press. 2004. Radev D, et al. MEAD – A Platform for Multidocument Multilingual Text Summarization. In 4th International Conference on Language Resources and Evaluation, 2004. Lisbon, Portugal. Prasetyo B, Uliniansyah T, Riandi O. SIDoBI: Indonesian Language Document Summarization System. In Proc. of International Conference on Rural Information and Communication Technology, 2009, pp. 378-382. Bandung, Indonesia. http://tcts.fpms.ac.be/synthesis/mbrola.html [Accessed May 26, 2011]

4. Conclusion In this research we have developed a prototype of Indonesian Automated Document Reader consisting of 3 main modules (OCR, Text Summarization, and TTS Synthesizer), with the main focus on text segmentation algorithms for both grayscale and color images. The text segmentation module combines Multivalued Image Decomposi-

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Ardimas Andi Purwita1, Trio Adiono2 School of Electrical Engineering and Informatics Electrical Engineering Department Institut Teknologi Bandung Bandung Indonesia e-mail: [email protected], [email protected]

Abstract urbo code is a high performance channel coding which is able to closely reach the channel capacity of Shannon limit. It plays an important role to increase the performance in one of the latest standard in the mobile network technology, such as Long Term Evolution (LTE).1 In this paper, Turbo code VLSI Architecture for LTE which is co-developed between Ministry of Communication and Information (Menkominfo) of Republic Indonesia and Microelectronics (ME) Laboratory Institut Teknologi Bandung (ITB), is discussed. The optimization is applied to reduce computational complexity and excessive memory requirement as well as the latency and delay. In order to increase the processing speed, 8-level parallel architecture is proposed. Furthermore, to increase the processing parallelization, we also applied the Max-log-MAP (Maximum A Posteriori Probability)2, 3, 4 , Sliding Window Algorithm (SWA)5, and dual bank Random Access Memory (RAM) for interleaver and deinterleaver block. Based on the simulation result, the proposed algorithm (encoder and decoder) is almost 16 faster than the original algorithm6 and 42 times smaller for the memory requirement (decoder). Additionally, the proposed algorithm reduces the size of interleaver and deinterleaver block by almost 50%. Lastly, in order to shorten the design cycle, the modeling method used to implement the algorithm and the architecture is System Level Modeling using SystemC. Moreover, the used verification method is Assertion Based Verification (ABV)7, 8 using System Verilog Assertion (SVA) with purpose of enhancing the level of confidence about the design.

T

Keywords: Turbo Code, 8-Level Paralellization, Dual Bank RAM, SystemC, ABV.

1. Introduction Communication system has been developing since the invention of Marconi’s wireless telegraphy to the digital wireless communication system, which is commonly used today. LTE, as the latest standard in the mobile network technology of the Third Generation Partnership

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Project (3GPP), represents a radical new step forward in wireless industry and increases in bit rate with respect to its predecessors by means of wider bandwidths and improved spectral efficiency.9 The performance of the communication system is measured by using data rate, Bit Error Ratio (BER), and packet error rate. Several methodologies have been

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developed to improve the performance of system near the channel capacity of Shannon limit 10, 11, 12. Turbo Code is one of the channels coding which is used to reduce received error signals in LTE technology 13. As described in technical specification of 1 LTE , the turbo encoder is composed of two Recursive Systematic Convolution (RSC) encoders concatenated in parallel. The number of data bits at the input of the turbo encoder is K, where 40 ≤ K ≤ 6114. Data is encoded by the first (i.e., upper) encoder in its natural order and by the second (i.e., lower) encoder after being interleaved. At the first, the two switches are in the up position as shown in Fig. 1. There are several problems in designing architecture of Turbo Code for LTE. Turbo code inherently has a large latency and low throughput due to the iterative process. Hence, highly parallel decoding architectures are required to achieve speed-up.

turbo decoder. Whereas, in the intention of implementing the speed up turbo encoder, we also propose 8-level parallelization for constituent encoder. The second problem in the implementation of high parallel turbo code is the interleaving delay. In order to speed up that delay, 8-level parallel processing and dual bank RAM is applied to generate index for interleaving process. In the previous cases, initially the implementation hardware (HW) design is separated to software (SW), and at the last step, both of them are combined together. As the result, this kind of process takes long time. Therefore, system level modeling using SystemC14 is applied in this research to shorten the time. In this paper, SW indicates the model as the reference of HW (RTL). Verification is the most crucial phase in the cycle of design. Along these lines, the most effective and efficient verification method is greatly required in order to save time, cost, and effort consumption. In this research, ABV is implemented to verify. The remainder of this paper is organized as follows. In the second section, method and experiment procedure is presented. In the next sections, the result is presented along with the discussion. At last, section four concludes the paper.

2. Method And Experiment Procedure

Figure 1. Turbo Encoder [1]

This paper discusses several key problems in the implementation of highly parallel turbo decoder. The first problem is the tradeoff between the performance and computational complexity of turbo decoder. The fundamental architecture of turbo decoder used in this paper is based on Valenti and Sun.6 In addition, the component decoder used is Max-log-MAP algorithm2] with channel model of Binary Phase Shift Keying (BPSK) modulation along with Additive White Gaussian Noise (AWGN) channel. However, one of the major problems of this algorithm is the excessive memory requirement. Therefore, the SWA is applied to increase the speed. We propose 8-level parallel processing in implementation of

The first step of this research is to figure out the specification of turbo code based on the technical standard for LTE1 with purpose of understanding the architecture. Afterward, the second step is to study the existing algorithm and architecture as the reference of the proposed architecture. Then, the third step is to study about the design techniques used in this research, which are system level modeling and verification. SystemC is used as system level modeling environment and ABV is used as verification method because of their advantages 14 7 8 . Fourth step is to design the optimized architecture. There are 8-level constituent encoders, 8-level index generators for interleaver and deinterleaver, 8-level Maxlog-MAP and SWA for component decoders, and usage of dual bank RAM in its top level architecture. In final step, the optimized architecture is modeled

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using SystemC. This functional model is verified by directly porting the turbo encoder, a channel, and the turbo decoder. The channel applied is BPSK channel along with AWGN. The verified model is observed by its BER performance. Afterward, the model is translated into RTL HW using Verilog. This RTL is verified with the functional model. Thereafter, the verification platform is implemented by combining constrained random stimulus input, assertion property, and its functional coverage.

3. Results And Discussion 3.1 The Proposed Turbo Code The proposed turbo encoder refers to Fig. 1, and we parallelize between the right and the left one, so that the dual bank RAM is used as shown in Fig. 2. It works in 8-level in parallel by processing 8 input bits instantaneously.

...(1) Yet, the proposed index generator for interleaver or deinterleaver is described in Eq. 2.

Figure 2. The Proposed Turbo Encoder

The proposed constituent encoder is described in Eq. 1.

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...(2) Both of equations are derived from original equatian stated in standard [1]. The techniques are common term reduction as shown in proof 1.

Whereas, top level architecture for turbo decoder is shown in Fig. 3 The component decoder is combination of SWA and Max-log-MAP algorithm. In the same way with the turbo encoder, it works 8-level in parallel both for SWA and Max-log-MAP algorithm with length block 40. The dashed line shows backward recursion metric computation

...(3)

Figure 3. The Proposed Turbo Decoder

Figure 4. The Proposed SWA and Max-log-MAP algorithm

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Figure 5. The BER performance, K = 200

Figure 6. The Timing Diagram

and the straight line shows forward recursion metric and LLR. The BER performance for K = 200 is shown in Fig. 5 with the BER target is 10-5. This result describes the

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desired condition. Therefore, the design is verified and ready to be translated into HW RTL. At the last, after implementing into HW RTL, Fig. 6 shows the result for K = 6144. The figure also shows counter value

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Reference Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Multiplexing and channel coding (Release 9), 3GPP TS 36.212 v9.2.0, June 2010. W. Koch and A. Baier, “Optimum and sub-optimum detection of coded data disturbed by time-varying intersymbol interference [applicable to digital mobile radio receivers],” in Global Telecommunications Conference, 1990, and Exhibition. ’Communications: Connecting the Future’, GLOBECOM ’90., IEEE, dec 1990, pp. 1679 –1684 vol.3. J. Erfanian, S. Pasupathy, and G. Gulak, “Reduced complexity symbol detectors with parallel structure for isi channels,” Communications, IEEE Transactions on, vol. 42, no. 234, pp. 1661 –1671, feb/mar/apr 1994. Figure 7. The ABV Implementation

which count the match output value with input bits. The figure describes that all of the output value are matched. Fig. 7 shows the result of ABV implementation. It shows that there are no fail properties. It is simple to conclude that if the output HW matches with the model and there are no fail properties, so that the design is totally verified.

4. Conclusion This paper has discussed optimized turbo code VLSI architecture for LTE using system level modeling and assertion based verification. The main purposes of this research are to reduce computational complexity and excessive memory requirement as well as the latency and delay. Besides the theory, this research also focuses on the modern design method to shorten the design cycle and to enhance the level of confidence. The problems are solved in this paper by combining the parallel architecture, SWA, Max-log-MAP algorithm, system level modeling, and ABV.

J. Erfanian and S. Pasupathy, “Low-complexity parallelstructure symbol-by-symbol detection for isi channels,” in Communications, Computers and Signal Processing, 1989. Conference Proceeding., IEEE Pacific Rim Conference on, jun 1989, pp. 350 –353. A. Viterbi, “An intuitive justification and a simplified implementation of the map decoder for convolutional codes,” Selected Areas in Communications, IEEE Journal on, vol. 16, no. 2, pp. 260 –264, feb 1998. M. C. Valenti and J. Sun, “The umts turbo code and an efficient decoder implementation suitable for software defined radios,” International Journal of Wireless Information Networks, vol. 8, pp. 203–216, 2001. H. Foster, A. Krolnik, and D. Lacey, Assertion-based design, Kluwer Academic Publishers, 2004. Thorsten Grotker, System design with systemc, Kluwer Academic Publishers, Norwell, MA, USA, 2002. EURASIP J. Wirel. Commun. Netw., vol. 2009, 2009. J. Hagenauer and P. Hoeher, “A viterbi algorithm with soft-decision outputs and its applications,” in Global Telecommunications Conference, 1989, and Exhibition. Communications Technology for the 1990s and Beyond. GLOBECOM ’89., IEEE, nov 1989, pp. 1680 –1686 vol.3. L. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal decoding of linear codes for minimizing symbol error rate (corresp.),” Information Theory, IEEE Transactions on, vol. 20, no. 2, pp. 284 – 287, mar 1974.

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L. Hanzo, Turbo Coding, Turbo Equalisation and Space-Time Coding for Transmission over Fading Channels. New York, NY, USA: John Wiley & Sons, Inc., 2002. R. H. Morelos-Zaragoza, The Art of Error Correcting Coding. John Wiley & Sons, 2006. Thorsten Grotker, System design with systemc, Kluwer Academic Publishers, Norwell, MA, USA, 2002.

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(YDOXDWLRQ RI )LQJHUSULQW 2ULHQWDWLRQ )LHOG &RUUHFWLRQ 0HWKRGV A. A. K. Surya1, A. S. Nugroho2 Faculty of Information Technology, Swiss German University, Indonesia1 Agency for the Assessment and Application of Technology (BPPT), Indonesia2 e-mail: [email protected], [email protected]

Abstract

T

he estimation of fingerprint Orientation Field (OF) plays an important role in most fingerprint feature extraction algorithms. Many of the later stages in fingerprint feature extraction process (e.g. ridge enhancement, singular points detection) utilize fingerprint OF information as a cornerstone, thus the far-reaching implication of its estimation to the whole recognition process. Unfortunately, the accurate and robust estimation of fingerprint OF in low-quality fingerprint images is difficult and still remains as a challenge until today. This research attempts to evaluate the effectiveness of two well-known fingerprint OF correction methods. The method based on low-pass filtering and least square approximation. The experimental results show that the classical filter-based method is very efficient in computation and has a comparable performance to the more recent method based on least square approximation. Keywords: fingerprint recognition, fingerprint orientation field, least square approximation, singular points

1. Introduction By year 2012, the civil administration system of Indonesia will adopt the use of Electronic National Identity Card (E-KTP) and biometric recognition technology to uniquely identify individual citizens. The adoption of the new technologies is motivated by the weaknesses adhered in the traditional civil administration system, e.g., possibilities for impostors to conduct identity frauds. Currently, the implementation of the new civil administration system in Indonesia mostly relies on foreign products. For the purpose of achieving a better technological maturity and autonomy of Indonesia as a

nation, a research on fingerprint recognition technologies has been initiated by the Agency for the Assessment and Application of Technology (BPPT), Indonesia. This research is a contribution to the research conducted by BPPT. A fingerprint image is characterized by the interleaved pattern of friction ridges of the skin and the valleys between them (see Fig. 1). Two different impressions of fingerprints can be compared by examining the salient features of the ridge structure to determine whether they are a match or distinct pair. The goal of the fingerprint feature extraction task is therefore to extract and describe a fingerprint image in terms of its salient

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features. According to 1, ridge characteristic can be examined in a hierarchical order at three different levels. At the global level (Level 1), ridge flow information can be used to describe the global pattern of a fingerprint. Global level features alone are often not sufficient to uniquely discriminate between individuals. They are commonly utilized for fingerprint classification or as an intermediate step to derive other features. Singular regions and fingerprint Orientation Field (OF) are among the set of features that can be examined at the global level. Singular regions are the landmarks in a fingerprint image, characterized by the high ridge curvature. Example of singular regions in a fingerprint image can be seen in Fig. 1, in which the two types of singular regions (loop and delta) are shown.

Loop

Delta

Figure 1: A Fingerprint Image and Its Orientation Field

Fingerprint OF is a matrix that encodes the ridge flow orientation in a fingerprint image (see Fig. 1). The estimation of fingerprint OF plays an important role in most fingerprint feature extraction algorithms. Many of the later stages in fingerprint feature extraction process utilize fingerprint OF information as a cornerstone (e.g. ridge enhancement, singular points detection), thus the far-reaching implication of its estimation to the whole recognition process. Unfortunately, an accurate and robust estimation of OF is difficult for low-quality fingerprint images. This is a challenge encountered by any fingerprint recognition system, especially if the system is deployed in developing countries. For the case in Indonesia, many of the citizens perform manual labor as an occupation, thus the presence

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of scars and creases in the finger skin is fairly common. Unreliable OF information can severely affect the whole recognition process. For this reason, after the coarse OF estimation has been obtained, a further post-processing step is needed to correct the estimation at corrupted regions. One of the most commonly used approach for OF estimation correction is based on lowpass filtering, e.g. using a box filter.2 Most methods based on low-pass filtering are cost-effective and relatively easy to implement. However, because these methods are based on local information, they are often ineffective against poor-quality fingerprint images, in which incorrect orientation information may dominate the correct ones. For this case, an OF correction based on global mathematical modeling might perform better. An example of a fingerprint OF correction approach based on mathematical modeling is proposed in 3, referred to as Fingerprint Orientation Model based on 2D Fourier Expansion (FOMFE). Based on the precomputed coarse OF, this model can approximate the global fingerprint OF using a trigonometric polynomial, whose parameters can be derived from least square approximation. The approach based on least square approximation using Fourier series as proposed in 3 is of interest because of its capability to approximate fingerprint OF without requiring a prior detection of singular points position. In this research, we attempt to evaluate the performance of this OF correction approach in terms of speed and effectiveness. As a comparison, the classical filter-based approach is also implemented and discussed.

S tart P reproces s ing

OF correction

S egmentation

S P detection

OF es timation

E nd

Figure 2: The Implemented Feature Extraction Algorithm

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2. Feature Extraction Algorithm Fig. 2 summarizes the feature extraction algorithm implemented in this research. In this research, the effectiveness of the implemented OF correction methods are measured using singular points (SP) detection rate. The SP detection approach implemented in this research is based on Poincaré index information 4. This approach is known to be sensitive to noises in the fingerprint OF, thus the resulting SP detection rate can reflect the quality of the estimated OF. In the preprocessing stage, the input fingerprint image is convolved with a Gaussian filter of size 5× 5. This operation is intended to suppress small noises and fingerprint pores, whose existence might reduce the quality of the estimated OF. The algorithm proceeds with a segmentation process to discriminate between the regions of interest in the fingerprint image from the background. After segmentation, the focus of the feature extraction algorithm can be narrowed down to the region of interest, thus unnecessary computation can be avoided and spurious SP detections can be reduced. In this system, the segmentation process is performed by utilizing local graylevel intensity information. The algorithm proceeds with the computation of coarse OF. There are several approaches that can be utilized to obtain the coarse OF, with the most prevalent being the gradient-based approach, particularly because of its computational efficiency and precision. In this system, the computation of coarse OF is performed by using the gradient-based approach as implemented in 5. In order to reduce computational cost, instead of assigning orientation information to each pixels in the image, the OF is discretized into a set of non-overlapping blocks, each of size 11×11 pixels. After the coarse OF has been obtained, a further post-processing step is needed to refine the estimation at corrupted regions. In this research, two OF correction approaches are implemented and compared: the approach based on low-pass filtering (as implemented in 2) and least square approximation (as implemented in 3). For the approach based on low-pass filtering, a box filter of size 3×3 is employed as the filter. The number of filtering iterations is varied in order to demonstrate its

effect to the quality of the resulting OF. Least square approximation is a systematic approach to find an approximation model that describes the general trend of the data. In the case of fingerprint OF, the resulting approximation model can be utilized as a noise reduction tool. In this research, bivariate Fourier series are employed as the basis for the approximation function. Fourier series is employed because of its capability to approximate data with high-curvature characteristic, which is the case for fingerprint OF. In this research, the degree of expansion K of the series will be varied in order to demonstrate its effect to the quality of the resulting OF. For further technical details regarding these two OF correction approaches, please consult the corresponding publications, 2 and 3. After OF correction, the algorithm proceeds with the SP detection step. In this system, an approach based on Poincaré index is utilized for the detection of SP. Please consult 6 for further technical details regarding SP detection based on Poincaré index. In a nutshell, based on this information, the location of the loop and delta-type SP can be determined.

3. Experimental Results 3.1 Performance Evaluation Approach As previously mentioned, we use singular points detection rate to measure the performance of the fingerprint OF correction methods. In this research, we utilize recall, precision, and F 1 as the metrics to measure the effectiveness of an SP detection. Recall R measures the proportion of the correctly detected SP to the ground-truth SP. Precision P measures the proportion of the correctly detected SP to the overall detections. TP

R= TP + TN

...(1)

P= TP+TPF P

...(2)

where TP (True Positive) is the number of SP correctly detected, TN (True Negative) is the number of missed SP, and F P (False Positive) is the number of points falsely recognized as SP. For the case of SP detection, R reflects the accuracy and P reflects the noise-reduction capability. These metrics can be summarized using F 1 , the

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harmonic mean between recall and precision. In this research, the locations of ground-truth SP are manually labeled. We set the distance threshold between a detected SP to ground-truth SP to be 20 pixels. A detected SP will be considered as a true positive detection if its distance to the nearest ground-truth SP is below the specified threshold. C oars e

F ingerprint

3.2 Experimental Data The fingerprint images used in this experiment are obtained from The Directorate General of Population and Civil Registry, a subdivision under the Ministry of Home Affairs of the Republic of Indonesia. The fingerprint images are electronically captured using a live-scan fingerprint sensor. All of the fingerprint images are of size 480×320 pixels. In this experiment, 300 fingerprint images are used as experimental data. Based on visual inspection, these images are roughly divided into 2 categories (150 images for each category): good-quality fingerprint with a clear overall ridge structure and poor-quality fingerprint, characterized by scars, smudges, creases, etc. Singular Points Detection Benchmark We compared the SP detection rate for the coarse OF and the OF corrected using the following two methods: Filter-based method (with box filter in 1, 3, 5, 7, and 9 iterations) and Approximation-based method (with Fourier basis function and K set to 3, 4, 5, 6, and 7). Fig. 3 illustrates the OF correction and SP detection results for a poor-quality fingerprint image. The groundtruth SP and the detected SP are marked as O (red) and X (blue), respectively. Table 1 summarizes the average precision, recall, and F 1 for all sample fingerprints. Several points can be deduced from the SP detection results: The filter-based method tends to shift the position of the SP as the number of iterations increases (see the SP shift from Filter-1 to Filter-9 in Fig. 3), resulting in more missed SP. This statement is shown by the decreasing precision and recall value of the filter-based method as the number of iterations increases. Filter-1 performs the best for good-quality fingerprint and Filter-3 performs the best for poorquality fingerprint. This fact shows that a higher number of iterations is needed in order to reduce noises in the OF for poor-quality fingerprint image. For good-quality fingerprint image, the

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F ilter-1

F ilter-5

F ilter-9

F ourier-3

F ourier-5

F ourier-7

Figure 3: Illustration of OF Correction and SP Detection Result

approximation-based method tends to increase the performance as the degree of expansion K increases. This fact shows that the higher K , the approximation model can better preserve the position of the SP (see the SP shift from Fourier-3 to Fourier-7 in Fig. 3). For poor-quality fingerprint image, the approximation-based method tends to increase the performance up to K = 5 , and then the performance declines. This is particularly caused by model-overfitting problem, which reduce the noise-reduction capability of the model (see the spurious SP detections for Fourier-7 Table 1: Average SP Detection Results

Method Coarse Filter-1 Filter-3 Filter-5 Filter-7 Filter-9 Fourier-3 Fourier-4 Fourier-5 Fourier-6 Fourier-7

Good-quality

Poor-quality

P

R

F1

P

R

F1

0.66 0.93 0.89 0.81 0.69 0.58 0.72 0.86 0.91 0.92 0.92

0.95 0.93 0.86 0.77 0.63 0.53 0.68 0.84 0.90 0.93 0.95

0.73 0.92 0.86 0.78 0.64 0.54 0.69 0.84 0.89 0.90 0.91

0.19 0.70 0.80 0.74 0.67 0.60 0.67 0.79 0.79 0.78 0.73

0.94 0.91 0.82 0.71 0.65 0.57 0.69 0.84 0.88 0.92 0.92

0.28 0.74 0.80 0.71 0.65 0.56 0.67 0.79 0.81 0.81 0.77

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References

in Fig. 3).

3.4 Speed Benchmark In this experiment, the speed test is conducted on a notebook PC with Intel Core 2 processor, running Ubuntu Linux 11.04 as the operating system. The fingerprint feature extraction system is written using C++ programming language. The speed test is conducted by observing the time required to finish the execution of the feature extraction system. Table 2: Average Execution Time

Method Coarse Filter-3 Filter-4 Filter-5 Filter-6 Filter-7

Time (s)

Method

Time (s)

0.024 0.024 0.027 0.025 0.027 0.023

Fourier-3 Fourier-4 Fourier-5 Fourier-6 Fourier-7

0.112 0.169 0.241 0.335 0.446

Table 2 summarizes the average execution time for each OF correction methods. From the table, it can be seen that the computational effort for the filter-based method is almost negligible. This is particularly caused by the small size of the matrix on which the filtering operation is applied (approximately 43×29 elements). The method based on least square approximation, on the other hand, is very expensive in terms of computation. This is particularly caused by the large number of operations needed to compute the regression coefficients.

Maltoni D, Maio D, Jain AK, and Prabhakar S. Handbook of Fingerprint Recognition, 2nd ed. Springer, 2009. Hong L, Wan Y, and Jain AK. Fingerprint image enhancement: Algorithm and performance evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1998. 20(8): 777789. Wang Y, Hu J, Phillips D. A fingerprint orientation model based on 2D Fourier expansion (FOMFE) and its application to singular-point detection and fingerprint indexing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007. 29: 573-585. Kawagoe M and Tojo A. Fingerprint pattern classification. Pattern Recognition. 1984. 17(3): 295-303. Liu M, Jiang X, and Kot AC. Fingerprint reference-point detection. EURASIP Journal on Applied Signal Processing. 2005. 4: 498-509. Jain AK, Prabhakar S, and Hong L. A multichannel approach to fingerprint classification. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1999. 21(4): 348-359.

4. Conclusion The OF correction method based on low-pass filtering has a comparable performance to the method based least square approximation as long as the number of iterations is kept to be small. In this experiment, the best performance is achieved with one iteration for good-quality fingerprint image and three iterations for poor-quality fingerprint image. Considering its computational efficiency, we recommend the use of the OF correction method based on low-pass filtering if the feature extraction module is to be installed in a real-time, large-scale Automated Fingerprint Identification System (AFIS).

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1RPLQDWRU 7,&$ &OXVWHU ,,

$OJRULWKP DQG $UFKLWHFWXUH 'HVLJQ RI 6RIW 'HFLVLRQ 'HPDSSHU IRU 6,62 '9% 7 E\ 8VLQJ 4XDGUDQW . %HVW Probo Aditya N.I., Trio Adiono Electrical Engineering Department, Institut Teknologi Bandung e-mail: [email protected], [email protected]

Abstract In this paper, we explain how to design the demapper for SISO DVB-T system. This soft decision method uses Log Likelihood Ratio algorithm to find error constellation because of AWGN noise. In this research, a new algorithm of likelihood detection for SISO system was found and called Quadrant K-Best (QK-Best). The algorithm focuses on quadrant to select the constellation point which belongs to it. Functional simulation of QK-Best algorithm uses Rayleigh channel model and AWGN for noise model. Architecture of DVB-T demapper was constructed based on QK-Best Algorithm and called Speaker Architecture. It has a working frequency of 39.19 MHz and latency of about 23 clock cycles. Speaker Architecture can also be implemented into Cyclone III FPGA series EP3C120F780C7N and solve the problems in 3 modulation modes (QPSK, 16QAM, and 64QAM). Key words: QK-Best, Demapper, DVB-T, Speaker architecture ©2011. Persatuan Pelajar Indonesia Jepang. All rights reserved.

1. Introduction Nowadays, digital TV becomes the major type of television and replace the domination of analogue TV in the market. DVB-T is one of the digital TV standards, and has two parts in its system, namely transmitter and receiver. When a data has been sent into a channel, it will be corrupted by noise from the channel. In DVB-T receiver, there are many blocks in the receiver to recover the data from noise or fading. Demapper has the role key to recover the modulation signal from the noise, which is transmitted through the medium.

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To build the module of DVB-T demapper, a likehood algorithm is needed. Maximum Likelihood Detector (MLD) is the first algorithm to solve the demapper problems by calculating the error distance using all constellation points on diagram. Due to the lack of efficient computation from MLD, K-Best algorithm was developed to reduce the computation. This algorithm, however, only operates in Multi Input Multi Output (MIMO) system. Since DVB-T is Single Input Single Output (SISO) system, a new K-Best algorithm made for SISO system is being developed, namely the Coordinate K-Best (CK-Best). CK-Best algorithm has the orientation

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to determine the window of constellation point by looking for the nearest coordinate of data.

… (2) Equation (2) can be re-written as: … (3)

2. Method and experiment procedure

H divider in Equation (3) is then changed to constant as follows: … (4)

This research began by algorithm exploration and design, continued by making MATLAB model to ensure the validity of QK-Best algorithm and then designing the architecture using the algorithm. An RTL design was made using Verilog language to test the demapper design.

2.1 QK-Best Algorithm QK-Best is a new algorithm to solve the demapper problem. This algorithm uses quadrant to select the constellation point for effectiveness. First step of the algorithm is to search the real constellation point location of received data by dividing the received symbol with channel response. The result is a complex number with the data location information.

Equation (4) is multiplied by quadratic constant H and written as: … (5) r


Simplified LLR LLR equation begins by logarithmic scale comparison between the alternative and null models: … (6) With assumptions that all symbols are equal and using the Bayes Law, Equation (6) becomes: … (7)

x x

x x

x x

Figure 1. Quadrant location to determine the constellation point

Next equation is made from density probability function for AWGN noise: … (8)

Figure 1 shows the probability in Quadrant I, Quadrant II, Quadrant III and Quadrant IV regions. Constellation point in the quadrant where data is occurred will be used and the other quadrant constellation points will be discarded.

Noise components are in-phase noise and quadrature noise which are assumed to be independent to each other: … (9)

2.2 Equations

Equation (8) and (9) are then combined: ... (10)

To reduce the hardware complexity, manipulation using mathematical equations are needed. The manipulation was divided into two parts of modified euclidean distance and simplified Log Likehood Ratio (LLR). r
Modified Euclidean Distance Euclidean equation is used in the Distance Calculation module on RTL design. Basic equation of demapper can be written as: … (1) Y=HS+n where Y is received symbol, H is channel fading, S is ideal constellation point, and n is AWGN.

And the combination of Equation (7) and (10) resulted in: ... (11)

Because of logarithm and sigma, RTL design is difficult to be implemented, and the approximate equation is used: … (12) Equation (11) and Equation (12) can be combined into a simplified equation of LLR

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… (13) This equation is ready to be used to make the output system becomes a soft type.

2.3 Speaker Architecture Next step was designing the architecture which combines QK-Best algorithm and LLR algorithm. The architecture must be pipelined for better frequency. The proposed architecture is shown in Figure 2 below. Figure 2 shows the Speaker Architecture which has the form of a speaker. First processing data comes

from YH* Multiplier as in Equation (4) to prepare input data for quadrant detector. Quadrant detector module has the function to determine the quadrant and give the next module a quadrant signal to generate the index. The next process is data calculation in the distance calculation module. After that, all of the complex error are changed to polarity form which has angle and magnitude. All magnitude of error are distributed depends on each bit and probability, and then the minimum value for each of them are decided. The value of probability zero is subtracted by the value of probability one. The result is a soft decision output which can be changed into hard or soft output.

rst clk

rst clk

rst clk

rst clk

error_real_1

y_real y_imag h_real h_imag constl mode_out rst clk

x_real x_imag hd

Y H* Multiplier rst clk

ind 1

Quad Detector

bit 25 generator

error_real_25 error_imag_1

ind 25

Calculation Distance

constl

Root Square

error_imag_25

rst clk

mode_out

rst clk

rst clk

rst clk

rst clk 0

enable 1 0

0 1

rst clk

rst clk rst clk

rst clk

rst clk

rst clk

rst clk

Fd 60

Min Search rst clk

Fd 61

bit LLR Min Search

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1

HO

0

SO

1

HO

0

SO

1

HO

0

SO

1

HO

0

SO

1

HO

0

SO

1

HO

0

rst clk

Min Search

Fd 50 rst clk

Fd 51

bit LLR Min Search

rst clk

Fd 40

Min Search rst clk

Fd 41

bit LLR Min Search

rst clk

Fd 30

Min Search rst clk

Fd 31

bit LLR Min Search

rst clk

Min Search

Fd 20

bit LLR

rst clk

Min Search

Fd 21 rst clk

Min Search

Fd 10 rst clk

bit LLR Min Search

Fd 11

rst clk

rst clk

0

Figure 2. Speaker Architecture

SO

data valid

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3. Results and discussion To know the performance of the algorithm and RTL design, a test was conducted and divided into two parts of simulation test. The first test used MATLAB software to find the performance of QK-Best algorithm. The performance of QK-Best was indicated by referencing the Bit Error Rate (BER) between that of MLD and QK-Best. The results are shown in Table 1.

Figure 4 shows that the Speaker Architecture had a working frequency of about 39.19 MHz and critical path of 25.518 ns. This architecture can be clocked less than or equal to 39.19 MHz.

Table 1. Performance QK-Best in Three Modulations

Table 2. Computation of Each Algorithm

Modulation

Total Data

Total Error

64QAM

514,080

2,735

16QAM

616,896

1,752

QPSK

822,528

444

Figure 4. Report timing of Speaker Architecture

Computation of Algorithm

Modulation

Referring to Table 1, the performance of QKBest can be calculated by dividing the total data with the total error multiplying symbol. BER of each modulation (QPSK, 16QAM, 64QAM) as a soft output had the values of about 2.7 10-4 (QPSK), 7.1 10-4 (16QAM) and 8.9 10-4 (64QAM), while their hard output did not have any error bit.

MLD

CK-

CK-Best

QK-

Best

LLR

Best 1

QKBest LLR

QPSK

4

4

4

4

16QAM

16

16

16

4

9

64QAM

64

31

40

16

25

Table 2 shows the comparison of algorithm computation for each modulation. QK-best algorithm showed the lowest computation of 75% to that of MLD.

4. Conclusion In this paper, it was shown that the QK-Best architecture theoretically reduces the computation by 75% to that of MLD in three modulation modes of 64QAM, 16QAM and QPSK. Matlab model of QK-Best exhibited a good performance which could be seen from its acceptable BER of 2.7 10-4 (QPSK), 7.1 10-4 (16QAM) and 8.9 10-4 (64QAM). The Speaker architecture has the working frequency of about 39.19 MHz and 23 clock cycles latency. Throughput demapper of the system was 1 data/clock.

Figure 3. Comparison of MATLAB and ModelSim simulation data in 16QAM mode

Figure 3 shows the simulation comparison of 16QAM modulation between MATLAB and ModelSim in hard output. It can be seen that there was no difference between them.

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References Altera Corp, Constellation Mapper and Demapper for WiMAX, Application Note 439, 2007. Axler S. Algebra and Trigonometry. John Wiley & Son, Inc. 2011. Ciletti M. Advanced Digital Design with the Verilog HDL. Pearson Prentice Hall. 2003. Haifang J. VLSI architecture of a K-Best detector for MIMO-OFDM wireless communication systems. Journal of Semiconductors. 2009. Vol. 30(7). Haykin S. Communication System, 4th Ed. John Wiley & Son, Inc. 2004. Murthy JVK, Chockalingam A. Log-Likelihood Ratio Based Optimum Mappings Selection of Symbol Mapping Diversity with M-QAM. 2005. Su K. Efficient Maximum Likelihood Detection for Communication Over Multiple Input Multiple Output Channels. Trinity Hall. 2005. Walpole R. Probability & Statistics for Engineers & Scientist, 8th Ed. Pearson Prentice Hall. 2006. Versatile Inc. CK-Best untuk Sistem OFDM SISO dan MIMO. Bandung: 2010.

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1RPLQDWRU 7,&$ &OXVWHU ,,

$SSOLFDWLRQ RI $GDSWLYH 1HXUR )X]]\ ,QIHUHQFH 6\VWHP $1),6 IRU /XQJ &DQFHU 'HWHFWLRQ 6RIWZDUH Sungging Haryo W.1, Sylvia Ayu P.2, M. Yusuf Santoso3, Syamsul Arifin4 Department of Engineering Physics, Sepuluh Nopember Institute of Technology, e-mail: [email protected], [email protected], [email protected], [email protected]

Abstract

L

ung cancer is one of the leading cause of death. Symptoms of the disease usually do not appear until the disease has progressed. Thus, early detection is not simple. Chest X-Ray test usually will be the first test for any masses or spots on the lungs. This paper presented medical prognosis using Adaptive Neuro Fuzzy Inference Systems (ANFIS) to predict lung cancer. Medical history such as characteristic data and pulmonary x-ray image of suspected patient are used as input of prediction software with two and three membership functions. After, it is validated by comparing training and testing results with doctor analysis. Moreover the results indicate that the software has detection performance with 96% accuracy in medical history prediction and 90% in imaging test.

Keywords: Lung Cancer, ANFIS, Diagnosis, X-Ray

1. Introduction Lung cancer is a disease characterised by uncontrolled cell growth in tissues of the lung.1 It is also the most preventable cancer.2 Cure rate and prognosis depend on the early detection and diagnosis of the disease.3 Lung cancer symptoms usually do not appear until the disease has progressed. Thus, early detection is not easy. Many early lung cancers were diagnosed incidentally, after doctor found symstomps as a results of test performed for an unrelated medical condition. Lung Cancer can be detected from an X-Ray (Rontgen) Scan. Scan results are

converted into digital data using image processing. This digital data will also be supported with doctor analysis for diagnosis. The Doctor has a key position as human expert for determining the basic rules and diagnosing lung cancer. During diagnosis process, subjectivity of the doctor is one of important obstacles. It is noteworthy that the decision of the doctor is related to the previous diagnosis. Then, to gain the precise diagnosis and interpret the x-ray scan accurately, previous input and output data diagnosis should be automated and used effectively. This research is aimed at designing an artificial intelligence system based on Adaptive Neuro Fuzzy Inference System for Lung Cancer Diagnosis.

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1.1 Lung Cancer Classification Lung Cancer is classified into two groups; i.e. Primary Lung Cancer and Secondary Lung Cancer. Primary Lung Cancer itself is further classified into two main types : (1) Non-Small Cell Lung Cancer (NCLC) and (2) Small Cell Lung Cancer (SCLC). SCLC is a type of small cell in large quantity, with a rapid cell growth. Some instances of medial therapy for SCLS are Chemotherapy and Radiation Therapy. On the other hand NSCLC is a singular cell growth, but this type of cell often might attack more than one lung area. Secondary Lung Cancer is emerged by the effect of cancer from other organs. Usually started with breast cancer and/or intesties cancer, then it is spreaded through blood circulation, speele system, or closect organ effect.4-6

1.2 Lung Cancer Diagnosis Process. The Process of lung cancer diagnosis depends on several factors : i.e. medical history (personal smoking and secondary expssure, past problem lungs record, current symptoms, activity background, and family history) 7 and physical examination (fever, strange breath sounds, swollen lymph nodes, liver enlargements, hand/ feet/ face/ anklesswelled, changing of skin pigmen, muscle weakness). The results of each step influence the next step in the process. Considered on previous factors (medical history, presenting symptoms, and physical examination). Laboratory and imaging test will be included in the further diagnosis for patient who is suspected with lung cancer. a. Laboratory Testing The accuracy of cancer diagnosis based on cytology sputum examination is determined by specimen collecting method and the type and size of tumor. Overall results cytological examination may establish the diagnosis up to 90%. b. Imaging Test Imaging test is performed to determine if a cancer cell is present. Although progress has been made in field of radiology in scanning as a CT-Scan and MRI scan, a chest x-ray is often the first imaging study performed when primary or metastatic lung cancer is suspected. Objective of lung cancer staging is to help doctors determine the best treatments that are likely effective. It also helps to determine what course of the prognosis is likely to be. Lung cancer stage ranges from I to IV. TNM classification systems are used to determine

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lung cancer stage. TNM expressed the factor as folows: T (tumor characteristics including size, location, and local invasion), N (regional lymph node involvement), M (metastasis status).8

1.3 ANFIS The ANFIS is the abbreviation for adaptive neuro-fuzzy inference system. Actually, this method is like a fuzzy inference system with a back propagation that tries to minimize the error. The performance of this method is like both Artificial Neural Network and Fuzzy Logic.10 In both ANN and FL case, the input passes through the input layer (by input membership function) and the output could be seen in output layer (by output membership functions). Since, in this type of advanced fuzzy logic, neural network has been used. Therefore, by using a learning algorithm the parameters are changed until reach the optimal solution. Actually, in this type the FL tries by using the neural network advantages to adjust its parameters.11 From Figure 1 above, neuro-fuzzy systems consist

Figure 1 ANFIS structure

of five layers with different function for each layer. One layer is constructed from several nodes represented by square or circle. The Square symbolizes adaptive node. It means that value of parameter can be changed by adaption. While circle is non-adaptive node and has a constant value. 12 Equations for each value are described below : a. First Layer : All nodes in first layer are adaptive nodes (changed parameter), node function for first layer is : (1) O1,i 1,2

Ai

(x)

for i =

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(2) O1,i i = 3, 4

Bi-2

(y)

for

Where x and y are input of node i, Ai or Bi-2 are membership functions of each input concerning fuzzy set of A and B. Used membership function is generalized bell type (gbell). b. Layer 2 All nodes in this layer are non-adaptive (fixed parameter). Node function of second layer is : O2,i = wi

Ai

(x)

Bi

(x), i = 1,2

(3)

Each output stated the firing strength of each fuzzly rule. This function can be expanded when the premises consist more than two fuzzy sets. c. Layer 3 All nodes in layer 3 are non-adaptive type which show normalized firing strength function. Output ratio at node-i from previous layer toward all previous output. Node function of layer 3 is :

Adaptive network with five layers is equivalent with fuzzy inference systems of Takagi Sugeno Kang.13,14

2. Method And Experiment Procedure 2.1 Data Collection The Data of this research are divided into two types: (1) characteristic data and (2) x-ray data. The Characteristic data is used to identify patient’s lung cancer risk level. While x-ray data is used in further diagnosis of a suspected patient. Characteristic data consists of information about normal and infected patient. There are four variables in characteristic data for identification: amount of cigarette consumed per day, duration of smoking, occupation, and cough. While the x-ray data is classified into two groups: (1) normal x-ray and (2) positive x-ray. Before the x-ray data used as input in the software, it needs a preprocessing.

2.2 Pre-Processing

(4) O3,i =

=

, for i = 1,2

if more than two membership functions are constructed, function can be expanded by dividing with total number of w for all rules d. Layer 4 Each node in layer 4 is adaptive node with node function as follows: O4,i =

fi =

(pix + qiy + ri )

(5)

Where w is normalized firing strength from layer 3 and p, q, and r parameters represent adaptive consequents paramaters e. Layer 5 In this layer, there is only one fixed node for summing all input, function of layer 5 is: (6) O5,i

fi

(a)

(b)

Figure 2 X-Ray image of (a) normal lungs and (b) lung cancer

Figure 2 shows that x-ray image of positive lung cancer has wider white area than normal lung. From this phenomenon, mean of x-ray image color can be determined with the following steps: a. Scanning The purpose of scanning is to convert the original data to digital data. In the process of scanning, X-ray image of lung is separated into left and right lung. This process is aimed to see the average detail of each side of the lung. Once the data is transformed into digital data. The pixel of the image is arranged in such a way that it can be used as the input software. Permitted size on software is 160 x 80 pixel. b. Grey scaling

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Output from scanner can be loaded on software and detected as matrix. It will appear in the software matrix colour scale of x-rays. At this stage, grey scaling is needed to facilitate the computation on software by dividing RGB with 3. c. Normalization Normalization is the process of dividing all grey scaling value matrix with the largest value of the matrix. It aims to make all images input in the software has the equal size though brightness levels from different input so that the mean results may apply to all image. All numbers are in matrix normalization ranged from 0 to 1 d. Calculation of the average color After normalization matrix result obtained, the next step is taking a parameter that represents the matrix image. Normalization matrix is a matrix that contains a series of normalized colours. Based on the fact that lung cancer image has more pixels than normal, it can be determined by averaging the normalization matrix. Average value used as ANFIS input at diagnosis stage to predict whether a patient has cancer or not. White has the highest value of 255. Meaning that if more white colour exists on x-ray images, average of normalization matrix is larger and the result detection is more positive.

2.3 ANFIS Model Design The design model is obtained using MATLAB to determine the most suitable premise and consequent parameters applied to the software. Each input is divided into approaches with 2 and 3 membership functions. For each membership function, the data training process is obtained. Training data aims to find the smallest error. After small error is collected, then the premise and consequent parameters canibe determined from each membership function in the FIS Editor.

2.4 Software Design Software is designed to facilitate patients and doctors to identify lung cancer. In accordance with the order of examination, the software is divided into three phases : screening, diagnosis, and stagging. a) Filtering Phase Software Filtering software is used to observe the influence level of the patient’s habit against lung cancer. This data uses characteristic data as examination parameter. ANFIS

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software is utilized in this process b) Diagnosis Phase Software Diagnostic software consists of three main phases: (1) image loading; (2) initial diagnosis; and (3) advanced diagnosis. Negative or positive value of cancer in the lung determined using ANFIS. c) Stagging phase software Stagging is the final step in staging cancer examination. By stagging phase doctor’s recommendations can be drawn.

2.5 Software Validation The aim of the software validation is to measure the accuracy of the software to predict lung cancer. The method used to validate the software is by comparison between doctor’s diagnosis and the results of the prediction software.

3. Result And Discussion In constructing filtering software for medical history data, two membership functions and three membership functions are applied. Software training is needed to determine smallest error for each membership function. For two membership functions, 16 basic rules constructed from four inputs and two clusters. Errors are steady and reached 2,9061 x 10-5 after 500 epoch training. While three membership functions have 81 rules. Then, 100 epochs for training are selected. In 100t epoch, the smallest error obtained is 3,9333 x 10-7. In diagnosis software, two inputs are clustered into 2 and 3 membership functions. Blue node at figure 5 below represents the number of rule. For two membership functions smallest error was reached at 0,14755 from 1000 epoch. While in three membership function 0,12734 was obtained as smallest error. Both filtering and diagnosis software are trained to maintain premise and consequent parameter. In filtering software, 50 data are used for two and three membership functions. Based on the software validation results, filtering training data with two membership function has 100% accuracy compared with doctor diagnosis (data expert). On the other hand, three membership function training data is 98% accurate. Thirty five samples were processed in diagnosis data training. For two membership functions, three test samples showed different result with doctor diagnosis. Three membership

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function showed better performance with only 1 error from 35 samples. In the testing process, both software validated by comparing between prediction of the software and data expert. In filtering software, from 25 samples, there were two errors in 2 membership functions. Better results obtained in three membership functions with only 1 error. Twenty samples were tested and compared in diagnosis software. After it was validated, software with two membership functions has better performance with two errors from 20 tests, while 3 errors existed in software with three membership functions. The result of validation training and testing data shown in table 1 below. Table 1 Result of training and testing validation software DATA Medical History

TRAIN TEST

X-Ray

TRAIN

Scan

TEST

MF

RMSE

VAF

(%)

2 3 2 3 2 3 2 3

0,49921 0,60747 0,41548 0,34804 0,36419 0,29113 0,25199 0,27523

52,3337 57,3042 66,6048 54,1573 48,8421 56,5538 65,4569 62,4914

100 98 92 96 91,4 97,2 90 85

RMSE is used for measuring the differences between values predicted by a model or an estimator and the values actually observed from the thing being modeled or estimated. All data with RMSE less than 0,5 are well precised.

4. Conclusion The current research has been carried out for developing powerful grade estimation tool for lung cancer diagnosis. By using Adaptive Neuro Fuzzy Inference Systems was resulted. The software is proven to be able to provide the actual model by using both neural systems and fuzzy logic. Based on the experiment, filtering prediction performed the best performance with 100% accuracy in training and 96% in testing. For diagnosis prediction, the best training accuracy was 97,2 % and 90 % accuracy for testing.

References American Society of lung cancer. Lung Cancer Non-Small Cel Overview. American Cancer Society. 2011: 1. American Cancer Society. Lung Cancer. URL:http://cancer. org accesed on July 30, 2011 Anonymous. Kanker Paru Pedoman Diagnosis dan Penatalaksanaan di Indonesia. Perhimpunan Dokter Paru Indonesia. 2003 : 2. Balachandran K, Anitha R. Supervised Learning Processing Techniques for Pre-Diagnosis of Lung Cancer Disease. International Journal of Computer Applications. 2010, 1(4): 17 Floche. Background information Non-small Lung Cancer. UR L :htt p://w w w.roche.co.id/f mf i les/re7229001/ Indonesian/media/background.library/oncology/lc/Lung. Cancer.Backgrounder.pdf/ accesed on July 30, 2011 American Society of Clinical Oncology. Guide to Lung Cancer. Alexandria. Conquer Cancer Foundation. 2011: 2. Dhillon D Paul, Snead David RJ. Advanced Diagnosis of Early Lung Cancer. 2007 : 57 Reeve Dana. NCCN Guide Line for Patient. National Comperhensive Cancer Network. Fort Washington. 2010 : 9-11. . Mountain Clinton F. Stagging Classification of Lung Cancer A Critical Evaluation. Clinic In Chest Medicine. 2002. 23(1): 104-107 Kadir Abdul. Identifikasi Tiga Jenis Bunga Iris menggunakan ANFIS. Forum Teknik. 2010, 3(1): 10 Kakar M, et all. Respiratory Motion Production by Using Adaptive Neuro Fuzzy Inference Systems (ANFIS). Institute of Physics Publishing. 2005, 50 : 4722. Cruz Adriano. ANFIS : Adaptive Neuro Fuzzy Inference Systems. Mestrado NCE. 2006 : 6. Chandana Sandep, Mayorga V Rene, Chan Christine W. Automated Knowledge Engineering. International Journal of Computer and Information Engineering. 2008, 2(6) : 373 Diah Iradiatu. Perbandingan antara ANFIS dan Observer Neural Network untuk Estimasi Kecepatan Motor Induksi Tiga Fasa. Jurnal Sains dan Teknologi. 2008, 6(2)

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