KLASIFIKASI DOSEN PEMBIMBING TUGAS AKHIR DENGAN METODE SUPPORT VECTOR MACHINE (SVM) TUGAS AKHIR

KLASIFIKASI DOSEN PEMBIMBING TUGAS AKHIR DENGAN METODE SUPPORT VECTOR MACHINE (SVM)

TUGAS AKHIR

Diajukan Untuk Memenuhi Persyaratan Guna Meraih Gelar Sarjana Strata 1 Teknik Informatika Universitas Muhammadiyah Malang

Lisa Noor Arida 201210370311043

JURUSAN TEKNIK INFORMATIKA FAKULTAS TEKNIK UNIVERSITAS MUHAMMADIYAH MALANG 2016 i

ABSTRAK Penentuan dosen pembimbing di Jurusan Teknik Informatika Universitas Muhammadiyah Malang masih dilakukan secara manual dengan mengandalkan pengetahuan pribadi tentang keahlian dosen yang dibutuhkan. Oleh karena itu dibutuhkan analisis tentang keahlian dosen yang sesuai dengan topik tugas akhir mahasiswa. Penelitian sebelumnya telah menerapkan metode Neural Network (ANN) untuk klasifikasi dosen pembimbing tugas akhir. Pada penelitian ini, metode klasifikasi dengan Support Vector Machine (SVM) akan digunakan untuk mengklasifikasi dosen pembimbing tugas akhir. Jenis SVM yang digunakan adalah multi class SVM one against one menggunakan kernel linear, polynomial, dan Radial Basis Function (RBF). Pengujian dilakukan dengan skenario menggunakan 2 metode processing data dan 2 macam parameter data. Metode preprocessing data pertama menggunakan stemming dan yang kedua dilakukan preprocessing data tanpa stemming. Selanjutnya, parameter data dibagi menjadi 2 macam. Pertama, parameter data yang digunakan adalah judul tugas akhir, abstrak dan keyword. Kedua, digunakan satu parameter data yaitu judul tugas akhir. Hasil pengujian dari perbandingan skenario dan ketiga kernel menunjukkan bahwa metode preprocessing data kedua dengan parameter judul tugas akhir, abstrak dan keyword menggunakan kernel polynomial dengan nilai cost (C) sama dengan 32 dan degree (d) sama dengan 2 menghasilkan performansi terbaik dengan rata-rata akurasi sebesar 28.10%, presisi sebesar 27.25%, recall sebesar 2.94%, f-measure sebesar 3.01%, dan running time 1.468 menit. Dari hasil pengujian ditunjukkan juga bahwa hasil akurasi, presisi, recall dan f-measure masing-masing skenario memberikan hasil yang belum signifikan pada setiap kernel sehingga dapat disimpukan bahwa pada penelitian tugas akhir ini, klasifikasi dosen pembimng tugas akhir dengan metode SVM belum dapat bekerja secara optimal. Kata kunci: klasifikasi dosen pembimbing, support vector machine, multi class SVM one agains one

ii

ABSTRACT In Informatics Engineering Department of Muhammadiyah Malang University, the advisors were chosen manually by relying on personal consideration about the qualification of the advisors. Therefore, an analysis about the qualification was needed to determine the advisors that were suitable for the students’ thesis. Previous research applied Neural Network (ANN) method in classifying the advisors, meanwhile, this research used Support Vector Machine (SVM). The type of SVM used was multi class SVM one against one. It used kernel linear, polynomial, and Radial Basis Function (RBF). The research was done by using 2 methods of data processing and 2 types of data parameter. First preprocessing method used stemming and the second was without stemming. Furthermore, data parameters were divided into two types. Firstly, data parameter were the thesis title, abstract, and keyword. Secondly, it only used one data parameter which was thesis title. The result of the study showed that the second data preprocessing with thesis title, abstract, and keyword as paramater which used kernel polynomial, the cost value was equal to 32 and the degree (d) was equal to 2. It showed the best performance with an average accuracy of 28.10%, 27.25% precision, 2.94% recall, 3.01% f-measure, and running time for 1,468 minutes. The conclusion was the result of accuracy, precision, recall, and f-measure did not give significant result to each kernel. In other words, the study of classifying the thesis advisors by using SVM did not work optimally. Keywords: classifying the advisors, support vector machine, multi class SVM one agains one

iii

LEMBAR PERSETUJUAN

KLASIFIKASI DOSEN PEMBIMBING TUGAS AKHIR DENGAN METODE SUPPORT VECTOR MACHINE (SVM)

TUGAS AKHIR Sebagai Persyaratan Guna Meraih Gelar Sarjana Strata 1 Teknik Informatika Universitas Muhammadiyah Malang

Disusun Oleh: Lisa Noor Arida 201210370311043

Menyetujui,

Pembimbing 1

Pembimbing II

Ali S. Kholimi, M.Kom NIDN : 070103202

Yuda Munarko, S.Kom., M.Sc. NIDN : 0706077902

Ali S. Kholimi, M.Kom NIP. 108.1410.0526

iv Yuda Munarko, S.Kom., M.Sc.

LEMBAR PENGESAHAN KLASIFIKASI DOSEN PEMBIMBING TUGAS AKHIR DENGAN METODE SUPPORT VECTOR MACHINE (SVM) TUGAS AKHIR Sebagai Persyaratan Guna Meraih Gelar Sarjana Strata 1 Teknik Informatika Universitas Muhammadiyah Malang

Disusun Oleh: Lisa Noor Arida 201210370311043

Tugas Akhir ini telah di uji dan dinyatakan lulus melalui sidang majelis penguji

Menyetujui, Penguji 1

Penguji II

Setio Basuki, M.T NIDN : 0714028403

Wahyu Andhyka K., M.Kom. NIDN : 0720068701 Mengetahui,

Ketua Jurusan Teknik Informatika Ali S. Kholimi, M.Kom NIP. 108.1410.0526 Yuda Munarko, S.Kom., M.Sc. Yuda Munarko, S.Kom., M.Sc. NIP. 108.0611.0443 NIDN : 0706077902

v

LEMBAR PERNYATAAN Yang bertanda tangan dibawah ini: NAMA

: LISA NOOR ARIDA

NIM

: 201210370311043

FAK/JUR

: TEKNIK/TEKNIK INFORMATIKA

Dengan ini saya menyatakan bahwa Tugas Akhir dengan judul “KLASIFIKASI DOSEN PEMBIMBING TUGAS AKHIR DENGAN METODE SUPPORT VECTOR MACHINE (SVM)” beserta seluruh isinya adalah karya saya sendiri dan bukan merupakan karya orang lain, baik sebagian maupun seluruhnya, kecuali dalam bentuk kutipan yang telah saya sebutkan sumbernya. Demikian surat pernyataan ini saya buat dengan sebenar-benarnya. Apabila kemudian ditemukan adanya pelanggaran terhadap etika keilmuan dalam karya saya ini, atau ada klaim dari pihak lain terhadap keaslian karya saya ini maka saya siap menanggung segala bentuk resiko/sanksi yang berlaku.

Malang, Agustus 2016 Yang Menyatakan

Lisa Noor Arida NIM : 201210370311043 Mengetahui, Pembimbing 1

Pembimbing II

Ali S. Kholimi, M.Kom NIDN : 070103202

Yuda Munarko, S.Kom., M.Sc. NIDN : 0706077902 vi

Ali S. Kholimi, M.Kom NIP. 108.1410.0526

LEMBAR PERSEMBAHAN Tugas akhir ini dapat diselesaikan berkat bantuan dari berbagai pihak yang turut serta berbagi doa dan dukungan. Untuk itu semua, saya persembahkan tugas akhir ini dan berterima kasih kepada: 1.

Allah SWT yang maha memberi petunjuk. Alhamdulillah Allah telah memberikan saya keteguhan hati dan kesabaran.

2.

Kedua orang tua saya, H. M. Noor dan Hj. Rozana Arida. Jika Allah berkenan menjadikan tiap huruf dalam tugas akhir ini sebagai kebaikan, maka kebaikan itu pertama-tama akan menjadi hak mereka.

3.

Dosen pembimbing Bapak Ali S. Kholimi dan Bapak Yuda Munarko yang selalu sabar dalam meberikan arahan.

4.

Pihak Dosen pengajar yang telah memberikan ilmunya beserta Staff TU Jurusan Teknik Informatika UMM.

5.

Teman-teman asisten Lab. Informatika UMM 2012. Terima kasih telah bersedia bertukar pikiran dan berbagi ilmu.

6.

Geng Hacker dan teman-teman seperjuangan Teknik Informatika 2012 UMM.

7.

Calon pasangan hidup, Ramadhana Fitryanto dan sahabat terbaik Siti Faridah Azmi yang selalu mendengarkan keluh kesah saya.

8.

Semua pihak yang tidak dapat saya sebutkan satu persatu yang telah berjasa dalam pengerjaan tugas akhir ini.

vii

KATA PENGANTAR Dengan memanjatkan puji syukur kehadirat Allah SWT. Atas segala limpahan rahmat dan hidayah-NYA sehingga peneliti dapat menyelesaikan Tugas Akhir yang berjudul: “KLASIFIKASI DOSEN PEMBIMBING TUGAS AKHIR DENGAN METODE SUPPORT VECTOR MACHINE (SVM)” Dalam penulisan tugas akhir ini disajikan pokok-pokok bahasan yang meliputi perancangan dan implementasi algoritma Support Vector Machine untuk klasifikasi dosen pembimbing tugas akhir. Saya menyadari sepenuhnya bahwa dalam penulisan Tugas Akhir ini masih banyak kekurangan dan keterbatasan. Oleh karena itu saya mengharapkan saran yang membangun agar tulisan ini bermanfaat bagi perkembangan ilmu pengetahuan kedepan.

Malang, Agustus 2016

Penulis

viii

DAFTAR ISI ABSTRAK .............................................................................................................. ii ABSTRACT ........................................................................................................... iii LEMBAR PERSETUJUAN................................................................................... iv LEMBAR PENGESAHAN .................................................................................... v LEMBAR PERNYATAAN ................................................................................... vi LEMBAR PERSEMBAHAN ............................................................................... vii KATA PENGANTAR ......................................................................................... viii DAFTAR ISI .......................................................................................................... ix DAFTAR GAMBAR ........................................................................................... xiii DAFTAR TABEL ................................................................................................ xvi BAB I PENDAHULUAN ..................................... Error! Bookmark not defined. 1.1

Latar Belakang......................................... Error! Bookmark not defined.

1.2

Rumusan Masalah ................................... Error! Bookmark not defined.

1.3

Batasan Masalah ...................................... Error! Bookmark not defined.

1.4

Tujuan Penelitian ..................................... Error! Bookmark not defined.

1.5

Metodologi .............................................. Error! Bookmark not defined.

1.6

Sistematika Penulisan .............................. Error! Bookmark not defined.

BAB II LANDASAN TEORI ............................... Error! Bookmark not defined. 2.1

Studi Literatur.......................................... Error! Bookmark not defined.

2.2

Text Mining.............................................. Error! Bookmark not defined.

2.3

Processing Data ....................................... Error! Bookmark not defined.

2.3.1

Case folding ..................................... Error! Bookmark not defined.

2.3.2

Remove Number & Punctuation....... Error! Bookmark not defined.

2.3.3

Tokenizing ........................................ Error! Bookmark not defined.

2.3.4

Stemming .......................................... Error! Bookmark not defined.

2.3.5

Stop Words Removal ........................ Error! Bookmark not defined.

2.3.6

Eliminating Extra Whitespace.......... Error! Bookmark not defined.

2.4

Pembobotan TF-IDF................................ Error! Bookmark not defined.

2.5

Klasifikasi ................................................ Error! Bookmark not defined.

2.6

Support Vector Machine (SVM) ............. Error! Bookmark not defined.

ix

2.6.1

Konsep SVM .................................... Error! Bookmark not defined.

2.6.2

SVM Linear ...................................... Error! Bookmark not defined.

2.6.3

SVM Non Linear .............................. Error! Bookmark not defined.

2.6.4

Multi Class SVM.............................. Error! Bookmark not defined.

2.7.

Estimasi Parameter .................................. Error! Bookmark not defined.

2.8

Metode Pengujian .................................... Error! Bookmark not defined.

2.8.1

Akurasi ............................................. Error! Bookmark not defined.

2.8.2

Presisi ............................................... Error! Bookmark not defined.

2.8.3

Recall................................................ Error! Bookmark not defined.

2.8.4

F-measure ........................................ Error! Bookmark not defined.

BAB III ANALISA DAN PERANCANGAN SISTEM ..... Error! Bookmark not defined. 3.1

Data Penelitian......................................... Error! Bookmark not defined.

3.2

Analisa Data ............................................ Error! Bookmark not defined.

3.3

Preprocessing Data ................................. Error! Bookmark not defined.

3.3.1.

Case Folding .................................... Error! Bookmark not defined.

3.3.2

Remove Number & Punctuation....... Error! Bookmark not defined.

3.3.3

Tokenizing ........................................ Error! Bookmark not defined.

3.3.4

Stemming .......................................... Error! Bookmark not defined.

3.3.5

Stop Words Removal ........................ Error! Bookmark not defined.

3.3.6

Eliminating Extra Whitespace.......... Error! Bookmark not defined.

3.4

Pembobotan TF-IDF................................ Error! Bookmark not defined.

3.5

Perancangan Pelatihan Klasifikasi SVM . Error! Bookmark not defined.

3.6

Perancangan Pengujian............................ Error! Bookmark not defined.

3.6.1

Skenario............................................ Error! Bookmark not defined.

3.6.2

Pengelolaan Data dalam Pengujian .. Error! Bookmark not defined.

3.6.3 Pemilihan dan Estimasi Parameter Terbaik ... Error! Bookmark not defined. 3.6.4

Pengujian Klasifikasi ....................... Error! Bookmark not defined.

BAB IV IMPLEMENTASI DAN PENGUJIAN .. Error! Bookmark not defined. 4.1

Implementasi Perangkat Lunak ............... Error! Bookmark not defined.

4.1.1

Persiapan Data.................................. Error! Bookmark not defined.

4.1.2

Preprocessing Data .......................... Error! Bookmark not defined. x

4.1.3

Pembobotan TF-IDF ........................ Error! Bookmark not defined.

4.1.4

Pembagian Data ............................... Error! Bookmark not defined.

4.1.5 Klasifikasi dengan Metode Support Vector Machine .............. Error! Bookmark not defined. 4.1.6 4.2

Metode Pengujian............................. Error! Bookmark not defined.

Hasil Pengujian Metode I ........................ Error! Bookmark not defined.

4.2.1

Kernel Linear ................................... Error! Bookmark not defined.

4.2.2

Kernel Polynomial ........................... Error! Bookmark not defined.

4.2.3 Kernel Radial Basis Function (RBF) ............. Error! Bookmark not defined. 4.3 Hasil Pengujian Metode II (Non Stemming) .......... Error! Bookmark not defined. 4.3.1

Kernel Linear ................................... Error! Bookmark not defined.

4.3.2

Kernel Polynomial ........................... Error! Bookmark not defined.

4.3.3 Kernel Radial Basis Function (RBF) ............. Error! Bookmark not defined. 4.4

Perbandingan Performansi Kernel ........... Error! Bookmark not defined.

4.4.1

Kernel Linear ................................... Error! Bookmark not defined.

4.4.2

Kernel Polynomial ........................... Error! Bookmark not defined.

4.4.3

Kernel RBF ...................................... Error! Bookmark not defined.

4.4.4

Perbandingan Kernel ........................ Error! Bookmark not defined.

4.5

Running Time Pengujian.......................... Error! Bookmark not defined.

4.6

Kesalahan Hasil Preprocessing Data ...... Error! Bookmark not defined.

4.7

Kesalahan Hasil Klasifikasi ..................... Error! Bookmark not defined.

4.8

Evaluasi dan Analisis Hasil ..................... Error! Bookmark not defined.

BAB V PENUTUP ................................................ Error! Bookmark not defined. 5.1

Kesimpulan .............................................. Error! Bookmark not defined.

5.2

Saran ........................................................ Error! Bookmark not defined.

DAFTAR PUSTAKA ........................................................................................... 75 LAMPIRAN ........................................................... Error! Bookmark not defined. Lampiran 1 Hasil Pengujian Kernel Linear Tahap I Metode I ............. Error! Bookmark not defined.

xi

Lampiran 2 Hasil Pengujian Kernel Linear Tahap II Metode I ........... Error! Bookmark not defined. Lampiran 3 Hasil Pengujian 1 Kernel Polynomial Tahap I Metode 1 . Error! Bookmark not defined. Lampiran 4 Hasil Pengujian 2 Kernel Polynomial Tahap I Metode 1 . Error! Bookmark not defined. Lampiran 5 Hasil Pengujian 3 Kernel Polynomial Tahap I Metode 1 . Error! Bookmark not defined. Lampiran 6 Hasil Pengujian 1 Kernel Polynomial Tahap II Metode I Error! Bookmark not defined. Lampiran 7 Hasil Pengujian 2 Kernel Polynomial Tahap II Metode I Error! Bookmark not defined. Lampiran 8 Hasil Pengujian 3 Kernel Polynomial Tahap II Metode I Error! Bookmark not defined. Lampiran 9 Hasil Pengujian 1 Kernel RBF Tahap I Metode I............. Error! Bookmark not defined. Lampiran 10 Hasil Pengujian 2 Kernel RBF Tahap I Metode I............. Error! Bookmark not defined. Lampiran 11 Hasil Pengujian 3 Kernel RBF Tahap I Metode I............. Error! Bookmark not defined. Lampiran 12 Hasil Pengujian 1 Kernel RBF Tahap II Metode I ........... Error! Bookmark not defined. Lampiran 13 Hasil Pengujian 2 Kernel RBF Tahap II Metode I ........... Error! Bookmark not defined. Lampiran 14 Hasil Pengujian 3 Kernel RBF Tahap II Metode I ........... Error! Bookmark not defined. Lampiran 15 Hasil Pengujian Kernel Linear Tahap I Metode II ........... Error! Bookmark not defined. Lampiran 16 Hasil Pengujian Kernel Linear Tahap II Metode II .......... Error! Bookmark not defined. Lampiran 17 Hasil Pengujian 1 Kernel Polynomial Tahap I Metode II Error! Bookmark not defined. Lampiran 18 Hasil Pengujian 2 Kernel Polynomial Tahap I Metode II Error! Bookmark not defined. Lampiran 19 Hasil Pengujian 3 Kernel Polynomial Tahap I Metode II Error! Bookmark not defined.

xii

Lampiran 20 Hasil Pengujian 1 Kernel Polynomial Tahap II Metode II Error! Bookmark not defined. Lampiran 21 Hasil Pengujian 2 Kernel Polynomial Tahap II Metode II Error! Bookmark not defined. Lampiran 22 Hasil Pengujian 3 Kernel Polynomial Tahap II Metode II Error! Bookmark not defined. Lampiran 23 Hasil Pengujian 1 Kernel RBF Tahap I Metode II ........... Error! Bookmark not defined. Lampiran 24 Hasil Pengujian 2 Kernel RBF Tahap I Metode II ........... Error! Bookmark not defined. Lampiran 25 Hasil Pengujian 3 Kernel RBF Tahap I Metode II ........... Error! Bookmark not defined. Lampiran 26 Hasil Pengujian 1 Kernel RBF Tahap II Metode II .......... Error! Bookmark not defined. Lampiran 27 Hasil Pengujian 2 Kernel RBF Tahap II Metode II .......... Error! Bookmark not defined. Lampiran 28 Hasil Pengujian 3 Kernel RBF Tahap II Metode II .......... Error! Bookmark not defined. Lampiran 29 defined.

Hasil Akurasi Kernel Polynomial ........... Error! Bookmark not

Lampiran 30 defined.

Hasil Presisi Kernel Polynomial ............. Error! Bookmark not

Lampiran 31

Hasil Recall Kernel PolynomialError! Bookmark not defined.

Lampiran 32 defined.

Hasil F-measure Kernel Polynomial ...... Error! Bookmark not

Lampiran 33

Hasil Akurasi Kernel RBF ........ Error! Bookmark not defined.

Lampiran 34

Hasil Presisi Kernel RBF .......... Error! Bookmark not defined.

Lampiran 35

Hasil Recall Kernel RBF .......... Error! Bookmark not defined.

Lampiran 36

Hasil F-measure Kernel RBF ... Error! Bookmark not defined.

DAFTAR GAMBAR Gambar 1.1 Metodologi ..........................................................................................3

xiii

Gambar 1.2 Desain Sistem .....................................................................................4 Gambar 2.1 Tahap Case Folding............................................................................8 Gambar 2.2 Tahap Remove Number & Punctuation ..............................................9 Gambar 2.3 Tahap Tokenizing ................................................................................9 Gambar 2.4 Tahap Stemming..................................................................................9 Gambar 2.5 Tahap Stop Words Removal ..............................................................10 Gambar 2.6 Tahap Eliminating Extra Whitespace ...............................................10 Gambar 2.7 Blok Diagram Klasifikasi [3]............................................................11 Gambar 2.8 Margin Hyperlane [11] .....................................................................13 Gambar 2.9 Soft Margin Hyperlane [11]..............................................................15 Gambar 2.10 Transformasi Vektor Input Ke Feature Search [11] ......................15 Gambar 3.1 Preprocessing Data...........................................................................21 Gambar 3.2 Proses Case Folding .........................................................................21 Gambar 3.3 Proses Remove Number & Punctuation............................................21 Gambar 3.4 Proses Tokenizing .............................................................................22 Gambar 3.5 Proses Stemming ...............................................................................22 Gambar 3.6 Proses Stop Words Removal .............................................................22 Gambar 3.7 Proses Remove Strip White Space.....................................................23 Gambar 3.8 Proses Pembobotan TF-IDF .............................................................23 Gambar 3.9 Proses Perancangan Klasifikasi SVM ..............................................24 Gambar 4.1 Source Code Case Folding ...............................................................30 Gambar 4.2 Data Tugas Akhir Sebelum Proses Case Folding.............................30 Gambar 4.3 Data Tugas Akhir Sesudah Proses Case Folding .............................30 Gambar 4.4 Source Code Remove Number & Punctuation..................................30 Gambar 4.5 Data Tugas Akhir Sebelum Proses Remove Number& Punctuation 31 Gambar 4.6 Data Tugas Akhir Sesudah Proses Remove Number & Punctuation 31 Gambar 4.7 Source Code Tokenizing ...................................................................31 Gambar 4.8 Data Tugas Akhir Sebelum Proses Tokenizing .................................31 Gambar 4.9 Data Tugas Akhir Sesudah Proses Tokenizing .................................32 Gambar 4.10 Source Code Stemming ...................................................................32 Gambar 4.11 Data Tugas Akhir Sebelum Proses Stemming ................................32 Gambar 4.12 Data Tugas Akhir Sesudah Proses Stemming .................................33

xiv

Gambar 4.13 Source Code Stop Words Removal .................................................33 Gambar 4.14 Data Tugas Akhir Sebelum Proses Stop Words Removal ...............33 Gambar 4.15 Data Tugas Akhir Sesudah Proses Stop Words Removal ...............33 Gambar 4.16 Source Code Eliminating Extra Whitespace...................................33 Gambar 4.17 Data Tugas Akhir Sebelum Proses Eliminating Extra Whitespace 34 Gambar 4.18 Data Tugas Akhir Setelah Proses Eliminating Extra Whitespace ..34 Gambar 4.19 Source Code Corpus Pembobotan TF-IDF.....................................34 Gambar 4.20 Source Code Pembobotan TF-IDF .................................................34 Gambar 4.21 Pembobotan TF...............................................................................34 Gambar 4.22 Pembobotan TF-IDF .......................................................................35 Gambar 4.23 Source Code Pembagian Data ........................................................35 Gambar 4.24 Source Code Klasifikasi SVM ........................................................36 Gambar 4.25 Source Code Perhitungan Akurasi ..................................................36 Gambar 4.26 Source Code Perhitungan Presisi ....................................................36 Gambar 4.27 Source Code Perhitungan Recall ....................................................36 Gambar 4.28 Source Code Perhitungan F-Measure.............................................36 Gambar 4.29 Grafik Kernel Linear Tahap 1 Metode 1 ........................................37 Gambar 4.30 Grafik Kernel Linear Tahap 2 Metode 1 ........................................38 Gambar 4.31 Grafik Pengujian 1 Kernel Polynomial Tahap 1 Metode 1 ............39 Gambar 4.32 Grafik Pengujian 2 Kernel Polynomial Tahap 1 Metode 1 ............40 Gambar 4.33 Grafik Pengujian 3 Kernel Polynomial Tahap 1 Metode 1 ............41 Gambar 4.34 Grafik Pengujian 1 Kernel Polynomial Tahap 2 Metode 1 ............41 Gambar 4.35 Grafik Pengujian 2 Kernel Polynomial Tahap 2 Metode 1 ............42 Gambar 4.36 Grafik Pengujian 3 Kernel Polynomial Tahap 2 Metode 1 ............43 Gambar 4.37 Grafik Pengujian 1 Kernel RBF Tahap 1 Metode 1 .......................44 Gambar 4.38 Grafik Pengujian 2 Kernel RBF Tahap 1 Metode 1 .......................44 Gambar 4.39 Grafik Pengujian 3 Kernel RBF Tahap 1 Metode 1 .......................45 Gambar 4.40 Grafik Pengujian 1 Kernel RBF Tahap 2 Metode 1 .......................46 Gambar 4.41 Grafik Pengujian 2 Kernel RBF Tahap 2 Metode 1 .......................47 Gambar 4.42 Grafik Pengujian 3 Kernel RBF Tahap 2 Metode 1 .......................47 Gambar 4.43 Grafik Kernel Linear Tahap 1 Metode 2 ........................................48 Gambar 4.44 Grafik Kernel Linear Tahap 2 Metode 2 ........................................49

xv

Gambar 4.45 Grafik Pengujian 1 Kernel Polynomial Tahap 1 Metode 2 ............50 Gambar 4.46 Grafik Pengujian 2 Kernel Polynomial Tahap 1 Metode 2 ............51 Gambar 4.47 Grafik Pengujian 3 Kernel Polynomial Tahap 1 Metode 2 ............52 Gambar 4.48 Grafik Pengujian 1 Kernel Polynomial Tahap 2 Metode 2 ............52 Gambar 4.49 Grafik Pengujian 2 Kernel Polynomial Tahap 2 Metode 2 ............53 Gambar 4.50 Grafik Pengujian 3 Kernel Polynomial Tahap 2 Metode 2 ............54 Gambar 4.51 Grafik Pengujian 1 Kernel RBF Tahap 1 Metode 2 .......................55 Gambar 4.52 Grafik Pengujian 2 Kernel RBF Tahap 1 Metode 2 .......................55 Gambar 4.53 Grafik Pengujian 3 Kernel RBF Tahap 1 Metode 2 .......................56 Gambar 4.54 Grafik Pengujian 1 Kernel RBF Tahap 2 Metode 2 .......................57 Gambar 4.55 Grafik Pengujian 2 Kernel RBF Tahap 2 Metode 2 .......................57 Gambar 4.56 Grafik Pengujian 3 Kernel RBF Tahap 2 Metode 2 .......................58 Gambar 4.57 Akurasi Kernel Linear ....................................................................59 Gambar 4.58 Presisi Kernel Linear ......................................................................59 Gambar 4.59 Recall Kernel Linear .......................................................................60 Gambar 4.60 F-Measure Kernel Linear ...............................................................60 Gambar 4.61 Akurasi Kernel Polynomial ............................................................61 Gambar 4.62 Presisi Kernel Polynomial ..............................................................62 Gambar 4.63 Recall Kernel Polynomial ...............................................................62 Gambar 4.64 F-Measure Kernel Polynomial .......................................................63 Gambar 4.65 Akurasi Kernel RBF .......................................................................64 Gambar 4.66 Presisi Kernel RBF .........................................................................64 Gambar 4.67 Recall Kernel RBF..........................................................................65 Gambar 4.68 F-Measure Kernel RBF ..................................................................65 Gambar 4.69 Perbandingan Kernel Tahap 1 Metode 1 ........................................66 Gambar 4.70 Perbandingan Kernel Tahap 2 Metode 1 ........................................66 Gambar 4.71 Perbandingan Kernel Tahap 1 Metode 2 ........................................67 Gambar 4.72 Perbandingan Kernel Tahap 2 Metode 2 ........................................68 Gambar 4.73 Contoh Hasil Kesalahan Processing Data ......................................70

DAFTAR TABEL Tabel 2.1 Hasil Penelitian Fatimah Wulandini dan Anto Satriyo Nugroho ............6

xvi

Tabel 2.2 Confusion Matrix ...................................................................................17 Tabel 3.1 Data Tugas Akhir ..................................................................................19 Tabel 3.2 Data Stoplist ..........................................................................................20 Tabel 3.3 Data Kata Dasar.....................................................................................20 Tabel 3.4 Skenario Pengujian ................................................................................25 Tabel 3.5 Pembagian Data .....................................................................................26 Tabel 3.6 Perancangan Pengujian Klasifikasi .......................................................27 Tabel 3.7 Perancangan Confusion Matrix .............................................................27 Tabel 4.1 Pengujian Dengan Kernel Polynomial ..................................................39 Tabel 4.2 Pengujian Dengan Kernel RBF .............................................................43 Tabel 4.3 Perbandingan Kernel .............................................................................68 Tabel 4.4 Running Time Pengujian .......................................................................69 Tabel 4.5 Kesalahan Hasil Klasifikasi Dengan Kernel RBF .................................70

xvii

DAFTAR PUSTAKA [1]

Aurangabadkar, S & Potey, M.A., 2014, Support Vector Machine Based Classification System for Classification of Sport Articles, IEEE International Conference on Data Mining Workshop.

[2]

Chang, Chih-Chung, Chih-Jen Lin, 2001, LIBSVM: A Library for Support Vector Machines. Department of Computer Science and Information Engineering, National Taiwan University.

[3]

Hermawati, Fajar Astuti, 2013, Data Mining, Penerbit Andi, Yogyakarta.

[4]

Hsu, Chih-Wei et al, 2004 A Practical Guide to Support Vector Classification, Department

of Computer Science

and Information

Engineering, National Taiwan University. [5]

Joachims, T., 2002, Learning to Classify Text using Support Vector Machines, Springer Science & Business Media, New York.

[6]

Khotimah, Husnul, 2014, Pemodelan Hybrid Tourism Recommendation Menggunakan Hidden Markov Model dan Text Mining Berbasis Data Sosial Media, Sekolah Pacasarjana, Institut Pertanian Bogor.

[7]

Muflikha, Lailil, dkk, 2013, Klasifikasi Kondisi Penderita Penyakit Hepatitis Dengan Menggunakan Metode Support Vector Machine (SVM), Program Teknologi Informasi dan Ilmu Komputer, Universitas Brawijaya.

[8]

Prasetyo, Eko, 2012, Data Mining: Konsep dan Aplikasi menggunakan Matlab, Penerbit Andi, Yogyakarta.

[9]

Rennie, JDM & Rifkin, Ryan, 2001, Improving Multiclass Text Classification with the Support Vector Machine, Massachusetts Insitute of Technology, Cambridge.

[10] Saraswati, N.W.S., 2011, Text Mining dengan Metode Naïve Bayes Classifier dan Support Vector Machine untuk Sentimen Analysis, Program Studi Teknik Elektro, Program pasca Sarjana Universitas Udayana, Bali. [11] Sembiring, Krisantus, 2007, Penerapan Teknik Support Vector Machine untuk Pendeteksian Intrusi pada Jaringan, Tugas Akhir Program Studi Teknik Informatika, Institut Teknologi Bandung, Bandung. [12] Sugiono, 2014, Metode Penelitian Kuantitatif, Kualitatif dan R&D, Cetakan ke-20, Bandung, Alfabeta.

75

[13] Syamsu, Rosalina, 2015, Implementasi Algoritma Neural Network dengan Metode Prune Untuk Klasifikasi Penentuan Dosen Pembimbing Tugas Akhir, Tugas Akhir Program Studi Teknik Informatika, Universitas Muhammadiyah Malang. [14] Quang, Anh tran et al. 2002. Evolving Support Vector Machine Parameters. Slide presentasi ICML. Tsinghua University. [15] Wahyuningstyas, Andita, 2016, Deteksi Spam pada Twitter Menggunakan Algoritma Naïve Bayes, Departemen Ilmu Komputer, Institut Pertanian Bogor. [16] Widodo, Prabowo Pudjo, dkk. 2013. Penerapan Data Mining dengan Matlab. Rekayasa Sains. Bandung. [17] Wulandini, F. & Nugroho A.S., 2009, Text Classification Using Support Vector Machine for Webmining Based Spatio Temporal Analysis of the Spread of Tropical Diseases, International Conference on Rural Information and Communication Technology. [18] Zinner, Thomas, dkk, Text Categorization for Deriving the Application Quality in Enterprises Using Ticketing, Springer International Publishing Switzerland, LNCS 9263, pp. 325-336. [19] Zhu, Yanwei, dkk, 2010, Improved Support Vector Machine Multiclassification Algorithm, Proceedings of the International Conference on Information Computing and Applications (ICICA 2010).

76

BIOGRAFI PENULIS Nama

: Lisa Noor Arida

NIM

: 201210370311043

TTL

: Barabai, 04 Maret 1995

Alamat Asal : Kompleh Hijrah Rasau Matang Ginalun Rt. 04 Rw. 02 Kec. Pandawan Kab. Hulu Sungai Tengah Provinsi Kalimantan Selatan No Hp

: +6281945730453

Email

: [email protected]

Riwayat Pendidikan Nama Sekolah

Tahun

MIN Ilung

2000 – 2006

MTsN Barabai

2006 – 2009

SMAN 1 Barabai

2009 – 2012

Universitas Muhammadiyah Malang

2012 – 2016

77