PUBLIKASI INTERNASIONAL PROSIDING

PUBLIKASI INTERNASIONAL PROSIDING

2nd International Conference on Engineering of Tarumanagara (ICET 2015) Faculty of Engineering, Tarumanagara University, Jakarta-Indonesia, 22-23 October 2015

PARALLEL SESSION SCHEDULE 22 October 2015 ROOM :1 MODERATOR : Klara Puspa Indrawati, S.T., M.Ars. SESSION :1 Paper No. Time Title/Author/Authors ID Performance Analysis in Home Industry Scale Production of Modified Traditional Brick as Green 1 13.00-13.15 AE-02 Building Material With Reed as Filler Kurniati Ornam, Masykur Kimsan, La Ode Ngkoimani Study of Staircases Design and Visitors’ Perception at 2 13.15-13.30 AE-10 Commercial Building Siti Belinda Amri, Santi, La Ode Abdul Syukur, Aspin Greenship Rating of Wood Materials in Building 3 13.30-13.45 AE-11 James Rilatupa Study of Bioclimatic Application to the Spatial Habitation 4 13.45-14.00 AE-12 Along the River Bank Ciliwung Handajani Asriningpuri, Ratih Budiarti, Harlisa Bornean Long House: Cosmological Value in Socio5 14.00-14.15 AE-14 Cultural Transformation Stream Klara Puspa Indrawati Potential Tour Toward Village of Cultural Conservation 6 14.15-14.30 AE-15 of Baluwerti, Surakarta, Jawa Tengah, Indonesia Naniek Widayati Priyomarsono Urban Sprawl Effect to Sustainable City 7 14.30-14.45 UE-03 Andas Budy Arrangement Model on the Sustainable Coastal 8 14.45-15.00 UE-04 Settlement in Makassar Naidah Naing, Asdar Djamereng, Bulgis

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PARALLEL SESSION SCHEDULE 22 October 2015 ROOM :1 MODERATOR : Ir. Hadian Satria Utama, MSEE SESSION :2 Paper No. Time Title/Author/Authors ID Implementation for Optimizing the Turnkey Project Scheme in Highrise Buiding/Flat Development Base on 1 13.00-13.15 UE-05 Duration and Interest Rate Sylvie Wirawati, Ricky Pittra Halim Spatial Articulation and Coexistence of Mode of Production in the Dynamics of Development at the 2 13.15-13.30 UE-06 Urban Fringe of Makassar City Batara Surya Sentiment Classification for Academic Questionnaire 3 13.30-13.45 EE-01 Using NBC and SVM Amir Hamzah, Naniek Widyastuti Analysis, Simulation and Implementation of Linear Block 4 13.45-14.00 EE-04 Codes Using a Microcontroller Joni Fat Visual Performance of Tunnel Lighting Along the Jakarta 5 14.00-14.15 EE-05 Outer Ring Road Endah Setyaningsih, Jeanny Pragantha Microcontroller Based Speed Controller of One Phase Induction AC Motor in Escalators 6 14.15-14.30 EE-06 Hadian Satria Utama, Edward Naulibasa Lie, Pono Budi Marjoko Implementation Hadoop on Private IaaS Cloud 7 14.30-14.45 EE-07 Computing Edy Kristianto

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PARALLEL SESSION SCHEDULE 22 October 2015 ROOM :2 MODERATOR : Dr. Widodo Kushartomo, S.Si., M.Si. SESSION :1 Paper No. Time Title/Author/Authors ID The Understanding and the Use of Motorcycle Special 1 13.00-13.15 CE-01 Stopping Space in Signalized Intersection Leksmono Suryo Putranto, Minggaza Suhindra Identification of Volcanic Rocks in Imogiri Yogyakarta 2 13.15-13.30 CE-02 Based on Subsurface Geologic Data Winarti, Hill Gendoet Hartono Model Test of Influence Groundwater Pumping to 3 13.30-13.45 CE-03 Decrease Surface of the Land Nurnawaty, M. Selintung, M.Arsyad Thaha, F. Marikar Flexural Behavior of Bamboo Reinforced Concrete Beams 4 13.45-14.00 CE-05 Ika Bali, Erianto Wijaya Identification of Hazardous Road Sections Using Over Dispersion-Excess Zero Data of Vehicle Accident at Johor 5 14.00-14.15 CE-06 Federal Roads Joewono Prasetijo, W Zahidah Musa, Zaffan Farhana Zainal Speed Profile Based on Design Consistency 6 14.15-14.30 CE-07 Joewono Prasetijo, Zaffan Farhana Zainal, W. Zahidah Musa Analysis Energy Consumption and Price of Fuel Truck in Makassar 7 14.30-14.45 CE-09 Mukhtar Lutfie, Lawalenna Samang, Sakti Adji Adisasmita, Isran Ramli Analysis of the Influence of Longitudinal Beam Toward the Concrete’s Nominal Shear Strength 8 14.45-15.00 CE-11 Daniel Christianto, Fannywati Itang, Widodo Kushartomo, and Irene Natasha Kosasih

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PARALLEL SESSION SCHEDULE 22 October 2015 ROOM :2 MODERATOR : Dr. Ir. M. Sobron Yamin Lubis, M.Sc SESSION :2 Paper No. Time Title/Author/Authors ID Model Reduction Methods for Cracked Rotor Dynamics 1 13.00-13.15 ME-02 Analysis Rugerri Toni Liong Simulation of Non-Newtonian Fluid Flow Through Encapsulation of 3-Dimensional Stacked Flip-Chip 2 13.15-13.30 ME-12 Package Using Lattice Boltzmann Method M.H.H. Ishak, M.Z. Abdullah, Aizat Abas, M.I. Ismail, M.S. Mohamad Cyclone Turbine Ventilator as a Power Source Lamps for Home Lighthing 3 13.30-13.45 ME-13 Jenny Delly, Welly Liku Padang, Baso Mursidi, Budiman Sudia Pathological Tremor Measurement and Reproduction 4 13.45-14.00 ME-15 Ping Yi Chan, Zaidi Mohd Ripin Effect of Tool Nose Radius on Surface Roughness for 5 14.00-14.15 ME-07 Machining ST 60 Steel Using Carbide Inserts Sobron Lubis, Erwin Siahaan, Teguh Imam Suyatno FSI Analysis on the Effect of Membrane Rigidity on Laminar Flow Separation Over NACA 643-218 Airfoil 6 14.15-14.30 ME-16 M.S. Abdul Aziz, M.Z. Abdullah, S.M. Firdaus, H. Yusoff, K.A. Ahmad, M. Zubair Experimental Investigation on Electronic Cooling Performance Using Porous Medium Heat Sink 7 14.30-14.45 ME-19 Muhammad Zaakir Angsoommuddin, Mohd Zulkifly Abdullah, Third Author Tensile Strength Polymer Matrix Composite With 8 14.45-15.00 ME-20 Reinforcement Gigantochloa Apus Sofyan Djamil, Mohamed Azlan Suhot, Mohd Zaki Hasan

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PARALLEL SESSION SCHEDULE 22 October 2015 ROOM :3 MODERATOR : Dr. Lamto Widodo, S.T., M.T. SESSION :1 Paper No. Time Title/Author/Authors ID Rapid Tooling Manufacturability Advanced Materials Using Automation Fuzzy-AHP Method for Injection 1 13.00-13.15 IE-01 Gyro-Magnetic Hot Mold Moh. Hardiyanto Designing a "Voice of Customer" Program to Support Knowledge-Based-QFD 2 13.15-13.30 IE-03 Ronald Sukwadi, Mokh. Suef, Enny Widawati, Cynthia Giovany Developing Algoritm to Design Jig & Fixture in SME Supporting Industry Based on Quality Function Deployment Methods (Case Study: Design Assy Machine 3 13.30-13.45 IE-06 and Leakage Testing Machine for Air Brake Coupling Hose) Cucu Wahyudin, Aan Mintarsih Comparison of Modular Layout and Distributed Layout 4 13.45-14.00 IE-07 Using Simulation Approach Trifenaus Prabu Hidayat, Andre Sugioko Optimization Replacement Schedule of Chisels Based on 5 14.00-14.15 IE-08 Quality Cost With Exponentially Increasing Failure Rate Dadang Arifin Re-Design The High of Handlebar on Semarang’s Bus Rapid Transit Using Anthropometry and Ergonomy 6 14.15-14.30 IE-09 Approach Annissa Lutfiah Hatuwe, Annisa Nindya Putri, Hanung Kurniawan, Reza Prisman Improvement of Service Performance in PO. Sandy Putra by Implementation of Safety Management System for 7 14.30-14.45 IE-13 Public Transport Hanung Kurniawan Designing a Closed Loop Tutelage System to Support 8 14.45-15.00 IE-14 Student in Preparing and Executing the Study Plan Andrijanto

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PARALLEL SESSION SCHEDULE 22 October 2015 ROOM :3 MODERATOR : Harto Tanujaya, S.T., M.T., Ph.D. SESSION :2 Paper No. Time Title/Author/Authors ID Heuristic Model With Discritized Time Horizon for Solving Alternative Machine Scheduling Problem on 1 13.00-13.15 IE-16 Single Operation Irwan Sukendar Embodiment Design of High Capacity Mixer (Case Study: Steamed Sponge Cake Production at “M” Home 2 13.15-13.30 IE-19 Industry) Adrian Christiady, Vivi Triyanti The Comparison of MTM-1 and MOST in Predicting 3 13.30-13.45 IE-20 Work Element Time Ivana Theresia Libardus, Vivi Triyanti Assessment of Outside Air Supply for Split AC System. 4 13.45-14.00 ME-04 Part B: Experiment K. D. Putra, A. Bimaridi, E. Djunaedy Assessment of Outside Air Supply for Split AC System. 5 14.00-14.15 ME-05 Part A: Affordable Instrumentation A. Bimaridi, K. D. Putra, E. Djunaedy Design and Construction of a Prototype of Screw Press for the Collection of Coconut Milk 6 14.15-14.30 ME-03 I Wayan Surata, Tjokorda Gde Tirta Nindhia, Davied Budyanto, Ahmad Eko Yulianto Finite Element Analysis of Modified In-Wheel Electric 7 14.30-14.45 ME-14 Motor for Hybrid Electric Motorcycle Didi Widya Utama Analysis Ethyl Ester in Biodiesel of Raw Material Waste 8 14.45-15.00 ME-17 Coconut Oil and Arak I Wayan Bandem Adnyana, Ni Made Suaniti Design and Development of Quadcopter Prototype 9 15.00-15.15 ME-18 Riyan Fenaldo Alphonso, Agustinus Purna Irawan, Frans Jusuf Daywin Effectivity of Heat Exchanger Using Coolant Fluid 10 15.15-15.30 ME-21 Harto Tanujaya

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PARALLEL SESSION SCHEDULE 23 October 2015 ROOM :1 MODERATOR : Mekar Sari, S.T., M.Sc. SESSION :3 Paper No. Time Title/Author/Authors ID Catholic Church: Influence of Liturgical Ritual in the Building Design (Studied on Four Catholic Churches in 1 09.00-09.15 AE-01 DKI Jakarta Area) Rudy Trisno, Sugiri Kustedja The Study of Defense Space on Chinatown Petak 2 09.15-09.30 AE-03 Sembilan, West Jakarta Nafi’ah Solikhah Survey on the Fulfillment of the Construction Requirements for Non-Engineered Houses in North 3 09.30-09.45 AE-04 Sumatra Darwin Reveal Knowledge Pacitan Rural Java Architecture 4 09.45-10.00 AE-05 Triyuniastuti, HB Satrio Wibowo, Sukirman Uniqueness Omah Dudur Dawa Architecture 5 10.00-10.15 AE-06 Satrio HB Wibowo, Sudaryono, E. Pradipto Global and Local, at the Same Time 6 10.15-10.30 AE-07 Franky Liauw Conducting Smart Programs in the Old Kampoeng Beyond the Modern Era City of Surabaya 7 10.30-10.45 AE-08 Danny Santoso Mintorogo Wanda K. Widigdo, Liliany S. Arifin, Anik Yuniwati Adaptation to Climate Change as the Controller of Disaster Vulnerability in Coastal Settlements in 8 10.45-11.00 AE-09 Mempawah Hilir, West Kalimantan Ely Nurhidayati Public Engagement in Public Space as the Elements of 9 11.00-11.15 AE-13 City Branding Olga Nauli Komala

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PARALLEL SESSION SCHEDULE 23 October 2015 ROOM :2 MODERATOR : Ir. Sofyan Djamil, M.Sc SESSION :3 Paper No. Time Title/Author/Authors ID Influence of Economic External Factors on Construction 1 09.00-09.15 CE-10 Project Duration Identification Basuki Anondho, Yusi Yusianto, Jemmy Wijaya Analysis Energy Usage and OTTV in the University 2 09.15-09.30 ME-06 Building Suci, Ery Djunaedy, M. Ramdlan Kirom Design Can Cover for Feeding Conveyor for Closing Cans in Cans Machine Maker 3 09.30-09.45 ME-08 Gusti Ryandi Arief, Agung, Wina Libyawati, Yohannes Dewanto Design of Cessna Aircraft Model JD - 010 Use Scale 8:1 Based Radio Control 4 09.45-10.00 ME-09 Muhammad Ahda, Ahmad Ilham Firdaus, Erick H.K., Yohannes Dewanto Magnet for Generate Electric Power Applications 5 10.00-10.15 ME-10 Dzulfi S Prihartanto, Alva Abdul Ganis, I.G.Eka Lesmana Dynamic Analysis on Conditions For Stay off Airfoil, 6 10.15-10.30 ME-11 Flying and Landing Bismil Rebetta, Aprilia Sakti, Erick H.K. Study of the Phenomenon of Collapse and Buckling the 7 10.30-10.45 ME-01 Car Body Frame Didik Sugiyanto, Audri Deacy Cappeberg Alitizing Process of Low Alloy Steel Emergency Doors in 8 10.45-11.00 ME-22 High Rise Building Erwin Siahaan

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PARALLEL SESSION SCHEDULE 23 October 2015 ROOM :3 MODERATOR : M. Agung Saryatmo, S.T., M.M. SESSION :3 Paper No. Time Title/Author/Authors ID Workload Analysis of Manually Operator at Clean and 1 09.00-09.15 IE-02 Tidy Car Wash Indra Surianto, I Wayan Sukania and Lamto Widodo The Strategy for Improving the Quality of Printing Film 2 09.15-09.30 IE-04 Production at PT X Lithrone Laricha S, Delvis Agusman, Lucky Research Comparison Among Business Incubator Research Sample and Analysis in the World 3 09.30-09.45 IE-05 Lina Gozali, Maslin Masrom, Habibah @ Norehan Haron, Teuku Yuri M. Zagloel Optimization of Production Planning Using Goal 4 09.45-10.00 IE-10 Programming Method (Study in a Cement Plant) Syamsul Anwar, Lonny Afrizalmi Risk Assessment of Distal Upper Extremity by Strain 5 10.00-10.15 IE-11 Index Method in a Small Shoes-Making Industry Syamsul Anwar, Yuri Fandi Tanjung The Effect of Working Environment Conditions on 6 10.15-10.30 IE-12 Employees’ Job Satisfaction in a Palm Oil Industry Elsa Febriani, Musdirwan, Syamsul Anwar Design of Eco-Friendly Dairy Farm Business Plan Using 7 10.30-10.45 IE-15 Business Canvas Model Meity Martaleo, William Bobby Susanto, Marcellia Susan Simulator of Pitot Tube, Using The Sensor MPX5100 in 8 10.45-11.00 IE-17 an Aircraft Model Joko Sugiarto, Dwijati, Hendardi, Yohannes Dewanto Ergonomic Aspect of Physical Environment in Junior High School (Between Individual Comfort and Saving 9 11.00-11.15 IE-18 Energy Behavior) Lamto Widodo, Fransisca Iriani, Endah Setyaningsih

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PARALLEL SESSION SCHEDULE 23 October 2015 ROOM :4 MODERATOR : I Wayan Sukania, S.T., M.T. SESSION :3 Paper No. Time Title/Author/Authors ID Decision Design Support System of Urban Landscape 1 09.00-09.15 UE-01 Planning Using 3D Interactive Visualization Herry Santosa, Shinji Ikaruga, Takeshi Kobayashi Sustainability Level of Settlement in Gajah Wong 2 09.15-09.30 UE-02 Riverside Area, Kotagede, Yogyakarta Abraham Bhaskara Singgih Analysis on the Needs of Bike Share in Institut Teknologi 3 09.30-09.45 CE-04 Sepuluh Nopember Surabaya Siera Rozanah, Ervina Ahyudanari DAB as an Effective Dispute Resolution in Construction 4 09.45-10.00 CE-08 Industry Purnomo Stabilization of Marine Dredged Sediment With 5 10.00-10.15 CE-12 Hydraulic Binders and Silica Fume Ernesto Silitonga GUI Applications on Ground Segment Research Rockets 6 10.15-10.30 EE-02 Imam Sampurno Nugroho, Yahan Nuryad, Nanniek Andiani, Yohannes Dewanto Document Subjectivity and Target Detection in Opinion 7 10.30-10.45 EE-03 Mining Using HMM Pos-Tagger Amir Hamzah, Naniek Widyastuti

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FOREWORDS CHAIRMAN OF THE ORGANIZING COMMITTEE First of all let’s pray and say thanks to God for giving us His mercy and blessings. The development of technology nowadays is growing up so fast. It aims to meet the necessary of the community to get a better life. Technological innovation which needed to develop technology products that can help people in improving their lives. Therefore the academics are not only required to implement the learning process, but also have to do research and community service to produce innovative scientific research. Indonesian academics today are eager to involve in research activities. Therefore we required a scientific forum for mutual discussion, exchange information about the research that has been carried out especially related to Urban Engineering. Faculty of Engineering, University of Tarumanagara conducts the second international conference to bring the academics, researchers to develop their knowledge and exchange ideas so that the researchers can improve the results of research that has been done. The conference called the 2ndInternational Conference on Engineering Tarumanagara, 2015, which is held on the Auditorium at the 8th floor of M Building, Campus I, University of Tarumanagara from 22 to 23 October 2015. The ICET 2015 conference theme is Urban Engineering for Future Generations. Future generations as the frontier of national development should be prepared from now on, along with the necessary infrastructure. The role of technology is to support the enhancement of the ability of future generations. This event includes to the presentation of scientific papers by keynote speakers, parallel sessions presenting papers of academics and research poster exhibition. The more extensive the information obtained, the more knowledge that we gained. Some papers submitted by researchers and academics from different countries such as, Germany, Malaysia will enrich the science and technological development. This conference proceedings contain the full text of all papers presented International Conference on Engineering of Tarumanagara 2015. Papers are categorized based on Engineering disciplines set by the organizing committee. Then, the presentation is divided into parallel sessions. On this occasion I would like to thank to: Foundation of Tarumanagara, Rector of Tarumanagara University, Dean of Faculty of Engineering Tarumanagara University and Sponsors of ICET 2015, for the support and help that has been given. I also would like to thank the authors for their contributions. Finally I would like to apologies if there are deficiencies in the activity. Thank you for all the attention.

I Wayan Sukania, S.T., M.T. Chairman of the Organizing Committee

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FOREWORDS DEAN OF FACULTY OF ENGINEERING I would like to warmly welcome all participants of the 2nd International Conference on Engineering of Tarumanagara (ICET 2015). This conference is organized by Faculty of Engineering, Tarumanagara University. The main aim of this conference was to respond the problem related to urban engineering for future generation. As this conference was designed to gather scientists, engineers, practitioners, and industries in engineering related disciplines, I expect intense discussion will happen among them so that some brilliant ideas to be used to improve the quality of human life can be produced. I hope this conference will create an international networking and collaborating, especially in engineering research and publication. I would like to congratulate the organizing committee of ICET 2015, for their outstanding efforts. I would also like to express my gratitude to the sponsors for their contributions in making this conference a resounding success. I wish the International Conference on Engineering of Tarumanagara (ICET 2015) a very useful and fruitful occasion. Thank you for your attention and contribution.

Prof. Dr. Agustinus Purna Irawan Dean of Faculty of Engineering

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SCIENTIFIC COMMITTEE 1. 2. 3. 4. 5. 6.

7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42.

Dr. Harto Tanujaya Prof. Hui Ming Wee Prof. Satoyuki Kawano Prof. Robert Vale Prof. Brenda Vale Dr. Ing. A. Rugerri Toni L.

Tarumanagara University, Indonesia (Chair) Chung Yuan Christian University, Taiwan Osaka University, Japan Victoria University of Wellington, New Zealand Victoria University of Wellington, New Zealand Karlsruhe Institut für Technologie, Karlsruhe Germany and Atma Jaya Catholic University of Indonesia, Indonesia Dr. Sie Teng Soh Curtin University, Australia Dr. Thomas Marconi Delft University of Technology (TU Delft), The Netherlands Prof. Yahaya Ahmad University of Malaya, Malaysia Prof. Nasir Bin Daud University of Malaya, Malaysia Prof. Mohd. Zulkifly b. Abdullah Universiti Sains Malaysia, Malaysia Prof. Zaidi Mohd. Ripin Universiti Sains Malaysia, Malaysia Dr. Ing. M. Razi Abdul Rahman Universiti Sains Malaysia, Malaysia Dr. Ing. Joewono Prasetijo Universiti Tun Hussein Onn, Malaysia Dr. Mohamed Azlan Suhot Universiti Teknologi Malaysia, Malaysia Dr. Bambang K. Hadi Bandung Institute of Technology, Indonesia Prof. Indarto University of Gadjah Mada, Indonesia Prof. Jamasri University of Gadjah Mada, Indonesia Prof. Tresna P. Soemardi University of Indonesia, Indonesia Prof. I Made Kartika University of Indonesia, Indonesia Prof. Danardono A. S. University of Indonesia, Indonesia Prof. Eddy S. Siradj. University of Indonesia, Indonesia Prof. Dr. Ir. Teuku Yuri M. Zagloel University of Indonesia, Indonesia Dr. Yono Reksoprodjo University of Indonesia, Indonesia Prof. Tjokorda Gde Tirta Nindhia Udayana University, Indonesia Prof. Ngakan Putu Gede Suardana Udayana University, Indonesia Prof. I Gusti Bagus Wijaya Kusuma Udayana University, Indonesia Prof. Kuncoro Diharjo Sebelas Maret University, Indonesia Prof. I Nyoman Pujawan Sepuluh November Institute of Teechnology, Indonesia Prof. Hadi Sutanto Atma Jaya Catholic University of Indonesia, Indonesia Dr. Iftikar Z. Sutalaksana Bandung Institute of Technology, Indonesia Prof. Tri Harso Karyono Tanri Abeng University, Indonesia Dr. Rianti Ariobimo Trisakti University, Indonesia Prof. Roesdiman Soegiarso Tarumanagara University, Indonesia Prof. Chaidir A. Makarim Tarumanagara University, Indonesia Prof. Agustinus Purna Irawan Tarumanagara University, Indonesia Prof. Leksmono S Putranto Tarumanagara University, Indonesia Dr. Adianto Tarumanagara University, Indonesia Dr. Agustinus Sutanto Tarumanagara University, Indonesia Dr. Danang Priatmodjo Tarumanagara University, Indonesia Dr. Naniek Widayati Tarumanagara University, Indonesia Dr. Titin Fatima Tarumanagara University, Indonesia

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ORGANIZING COMMITTEE Chairman Vice Chairman Conference Secretariat

I Wayan Sukania, S.T., M.T. Imma Sofi Anindyta, S.T., M.Arch. M. Agung Saryatmo, S.T., M.M. Mekar Sari, S.T., M.Sc. Didi Widya Utama, S.T. M.T.

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PROGRAM OVERVIEW Thursday, 22 October 2015 No

Time

Program

1 2

08.30-09.00 09.00-09.30

3 4

09.30-09.40 09.40-11.40

5 6 7 8 9 10 11

11.40-11.50 11.50-12.00 12.00-12.10 12.10-13.00 13.00-15.00 15.00-15.30 15.30-17.00

Registration Opening Ceremony a) Balinese Welcome Dance b) National Anthem + Mars Tarumanagara c) Chairman Speech d) Opening by Vice Rector of Academics and Student Affairs, Untar e) Photo Session (WRA, Dean, Chairman, Keynote Speakers, Presenters, Sponsors) Sponsorship Presentation Keynote Speaker 1: Prof. Zaidi Mohd. Ripin University Sain Malaysia, Malaysia Keynote Speaker 2: Ir. Irwansyah. Industrial Estate Association of Indonesia (Himpunan Kawasan Industri) Head of Environmental and Spatial Planning Appreciation to Keynote Speakers, Moderator, Sponsors) Sponsorship Presentation Technical Information Lunch Parallel Session I Coffee Break Parallel Session II

Friday, 23 October 2015 No

Time

1 2 3 4

08.30-09.00 09.00-11.00 11.00-11.15 11.15-12.00

Program Registration Parallel Session III Closing Ceremony Lunch

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TABLE OF CONTENTS Forewords: Chairman of the Organizing Committee Forewords: Dean of Faculty of Engineering Scientific Committee Organizing Committee Program Overview Table of Contents Parallel Session Schedules Invited Papers Development of Low Frequency Electromagnetic Vibration Energy Harvester Wan Masrurah Hairudin, M. Izudin Alisah, Chan Ping Yi, Tan Yee Hern, Zaidi Mohd Ripin

i ii iii iv v vi xii

1-8

List of Papers - Architecture Paper ID AE-01

AE-02

AE-03

AE-04 AE-05 AE-06 AE-07

AE-08

AE-09

AE-10

Title Author/Authors Catholic Church: Influence of Liturgical Ritual in the Building Design (Studied on Four Catholic Churches in DKI Jakarta Area) Rudy Trisno, Sugiri Kustedja Performance Analysis in Home Industry Scale Production of Modified Traditional Brick as Green Building Material With Reed as Filler Kurniati Ornam, Masykur Kimsan, La Ode Ngkoimani The Study of Defense Space on Chinatown Petak Sembilan, West Jakarta Nafi’ah Solikhah Survey on the Fulfillment of the Construction Requirements for Non-Engineered Houses in North Sumatra Darwin Reveal Knowledge Pacitan Rural Java Architecture Triyuniastuti, HB Satrio Wibowo, Sukirman Uniqueness Omah Dudur Dawa Architecture Satrio HB Wibowo, Sudaryono, E. Pradipto Global and Local, at the Same Time Franky Liauw Conducting Smart Programs in the Old Kampoeng Beyond the Modern Era City of Surabaya Danny Santoso Mintorogo Wanda K. Widigdo, Liliany S. Arifin, Anik Yuniwati Adaptation to Climate Change as the Controller of Disaster Vulnerability in Coastal Settlements in Mempawah Hilir, West Kalimantan Ely Nurhidayati Study of Staircases Design and Visitors’ Perception at Commercial Building Siti Belinda Amri, Santi, La Ode Abdul Syukur, Aspin

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pp 1

1-8

1

1 1 1 1

1

1

1-8


Paper ID AE-11 AE-12 AE-13 AE-14

AE-15

Title Author/Authors Greenship Rating of Wood Materials in Building James Rilatupa

pp 1-9

Study of Bioclimatic Application to the Spatial Habitation Along the 1-8 River Bank Ciliwung Handajani Asriningpuri, Ratih Budiarti, Harlisa Public Engagement in Public Space as the Elements of City Branding 1 Olga Nauli Komala Bornean Long House: Cosmological Value in Socio-Cultural 1-10 Transformation Stream Klara Puspa Indrawati Potential Tour Toward Village of Cultural Conservation of 1-10 Baluwerti, Surakarta, Jawa Tengah, Indonesia Naniek Widayati Priyomarsono

List of Papers - Civil Engineering Paper ID CE-01

CE-02

CE-03

CE-04 CE-05 CE-06 CE-07 CE-08 CE-09 CE-10

CE-11

Title/Author/Authors The Understanding and the Use of Motorcycle Special Stopping Space in Signalized Intersection Leksmono Suryo Putranto, Minggaza Suhindra Identification of Volcanic Rocks in Imogiri Yogyakarta Based on Subsurface Geologic Data Winarti, Hill Gendoet Hartono Model Test of Influence Groundwater Pumping to Decrease Surface of the Land Nurnawaty, M. Selintung, M.Arsyad Thaha, F. Marikar Analysis on the Needs of Bike Share in Institut Teknologi Sepuluh Nopember Surabaya Siera Rozanah, Ervina Ahyudanari Flexural Behavior of Bamboo Reinforced Concrete Beams Ika Bali, Erianto Wijaya Identification of Hazardous Road Sections Using Over DispersionExcess Zero Data of Vehicle Accident at Johor Federal Roads Joewono Prasetijo, W Zahidah Musa, Zaffan Farhana Zainal Speed Profile Based on Design Consistency Joewono Prasetijo, Zaffan Farhana Zainal, W. Zahidah Musa DAB as an Effective Dispute Resolution in Construction Industry Purnomo Analysis Energy Consumption and Price of Fuel Truck in Makassar Mukhtar Lutfie, Lawalenna Samang, Sakti Adji Adisasmita, Isran Ramli Influence of Economic External Factors on Construction Project Duration Identification Basuki Anondho, Yusi Yusianto, Jemmy Wijaya Analysis of the Influence of Longitudinal Beam Toward the Concrete’s Nominal Shear Strength Daniel Christianto, Fannywati Itang, Widodo Kushartomo, and Irene Natasha Kosasih

vii

pp 1-5

1-9

1-5

1 1-6 1-6 1-6 1 1-9 1-6

1-6


Paper ID

Title/Author/Authors

pp

CE-12

Stabilization of Marine Dredged Sediment With Hydraulic Binders and Silica Fume Ernesto Silitonga

1

List of Papers - Electrical Engineering Paper ID EE-01

EE-02

EE-03

EE-04

EE-05

EE-06 EE-07

Title/Author/Authors Sentiment Classification for Academic Questionnaire Using NBC and SVM Amir Hamzah, Naniek Widyastuti GUI Applications on Ground Segment Research Rockets Imam Sampurno Nugroho, Yahan Nuryad, Nanniek Andiani, Yohannes Dewanto Document Subjectivity and Target Detection in Opinion Mining Using HMM Pos-Tagger Amir Hamzah, Naniek Widyastuti Analysis, Simulation and Implementation of Linear Block Codes Using a Microcontroller Joni Fat Visual Performance of Tunnel Lighting Along the Jakarta Outer Ring Road Endah Setyaningsih, Jeanny Pragantha Microcontroller Based Speed Controller of One Phase Induction AC Motor in Escalators Hadian Satria Utama, Edward Naulibasa Lie, Pono Budi Marjoko Implementation Hadoop on Private IaaS Cloud Computing Edy Kristianto

pp 1-8

1

1

1-7

1-8

1-8 1-6

List of Papers - Industrial Engineering Paper ID IE-01

IE-02

IE-03

IE-04

IE-05

Title/Author/Authors Rapid Tooling Manufacturability Advanced Materials Using Automation Fuzzy-AHP Method for Injection Gyro-Magnetic Hot Mold Moh. Hardiyanto Workload Analysis of Manually Operator at Clean and Tidy Car Wash Indra Surianto, I Wayan Sukania and Lamto Widodo Designing a "Voice of Customer" Program to Support KnowledgeBased-QFD Ronald Sukwadi, Mokh. Suef, Enny Widawati, Cynthia Giovany The Strategy for Improving the Quality of Printing Film Production at PT X Lithrone Laricha S, Delvis Agusman, Lucky Research Comparison Among Business Incubator Research Sample and Analysis in the World Lina Gozali, Maslin Masrom, Habibah @ Norehan Haron, Teuku Yuri M. Zagloel

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pp 1-6

1-10

1-8

1-8

1-7


Paper ID

IE-06

IE-07

IE-08

IE-09

IE-10

IE-11

IE-12

IE-13

IE-14

IE-15

IE-16

IE-17

IE-18

IE-19

IE-20

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Combining Ground-based Data and MODIS Data for Rice Crop Estimation in Indonesia Conference Paper · December 2015 DOI: 10.1109/ICITSI.2015.7437710

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Combining Ground-based Data and MODIS Data For Rice Crop Estimation in Indonesia Sani M. Isa*, Suhadi Chandra**, Dyah Erny Herwindiati**, Sidik Mulyono*** *Master in Information Technology, Bina Nusantara University, Indonesia **Faculty of Information Technology, Tarumanaga University, Indonesia ***Agency for the Assessment & Application of Technology, Indonesia [email protected], [email protected], [email protected], [email protected]

Abstract— In this study, ground based data from spectroradiometer International Light type ILT900 combined with remotely sensed data from MODIS (Moderate Resolution Imaging Spectrometer) sensor of experimental farmland of the Ministry of Agriculture Republic of Indonesia in Sukamandi, Subang, West Java were used as input data for rice crop estimation using regression analysis. We chose four spectral bands (1-4) of MODIS data and four spectral bands of spectroradiometer data with same (the most similar) wavelength with chosen MODIS data. In addition to the spectral reflectance data, we also measured rice production data from several 7 x 20 plot areas that contain different rice varieties and different fertilizer compositions. The data from spectroradiometer then used for estimating regression model based on two approaches, Principal Component Regression (PCR) and Partial Least Square Regression (PLSR). The evaluation on ground-based data shows that PCR and PLSR give good accuracy with r2 = 0.968 and 984 respectively. Keywords— rice crop, remote sensing, MODIS, PCR, PLSR

I. INTRODUCTION Rice is the main dishes for over 90% Indonesians, that is why rice agriculture plays very significant role for the Indonesian society in economy, employment, and food security. A small declination of rice productivity could cause major effect to other sectors. Reliable agriculture information is one of important factors for strategic decision making by the producers, marketing, finance and government. The information such as crop distribution, area planted, and potential production is significant for all stakeholders in planning farming activities and marketing strategies. A conventional procedure to generate crop statistics mainly relies on the land survey. Survey results then aggregated by the hierarchical step from the village, district, and state level to provide acreage estimation. Survey approach is easily influenced by human factor, and also has low efficiency. The use of remote sensing technology for crop estimation has been started since early 1960s with the use of aerial photograph and digital processing technique. Remote sensing technology shows its potential after United States launched the first earth observing satellite Landsat in 1972. Several research have been carried out to show the capabilities of remote sensing for

crop estimation and forecasting e.g. Crop Identification Technology Assessment for Remote Sensing (CITARS) and Large Area Crop Inventory Experiment (LACIE). The result from LACIE for wheat production forecasting has met the 90/90 accuracy criteria for the US Plains. United States Department of Agriculture took the initiative to integrating Landsat data in domestic crop estimation programme on Statistic Reporting Service (SRS), which met 95/95 accuracy goals. Since then, large number of studies for crop monitoring and estimation have beed carried out in Europe, Africa, and other countries as well such as Argentina, Australia, Brazil, Canada, etc.) [1]. The approaches of land cover area estimation can be grouped into three categories: i) pixel counting and sub-pixel analysis that uses remote sensing as the primary source and ground data as auxiliary tool (for training data or sub-pixel analysis); ii) Regression, calibration and small area estimates which combining exhaustive information from satellite images and ground surveys; iii) Supporting area frame surveys. In this approach satellite images used to define sampling units, stratification, or quality control [2]. In this study, we used the first approach to estimate rice crop. Ground data from spectroradiometer International Light type ILT900 combined with remotely sensed data from MODIS (Moderate Resolution Imaging Spectrometer) sensor. We chose four spectral bands (1-4) of MODIS data and four spectral bands of spectroradiometer data with same (the most similar) wavelength with chosen MODIS data. The data from spectroradiometer were used for estimating regression model based on two approaches, Principal Component Regression (PCR) and Partial Least Square Regression (PLSR). To evaluate the accuracy of the regression models, MODIS data were used as test data and compared the result with actual data from the ground. II. METHODOLOGY A. Study Area We conducted survey for collecting spectral reflectance data using spectroradiometer at experimental farmland of the Ministry of Agriculture Republic of Indonesia in Sukamandi, Subang, West Java (6°41'04.0"S 107°37'47.5"E). The study

Figure 2. Data acquisition configuration for spectral reflectance measurement

area split into several plots using split plot experimental design. Each plot (7 x 20 meters) contains different rice variety and different fertilizer compositions. The measurements conducted eight times start from May to July 2012. Each measurement represents the spectral reflectance of rice plantation on each plot during eight stages (vegetative: seeding, tilering, stem elongation; reproductive: panicle initiation, heading, flowering; ripening: milk grain, dough grain, mature grain) of rice plantation growth. B. Ground Based Data The spectral reflectance of each plot in the survey area measured using spectroradiometer International Light type ILT900 completed with its accessories i.e. white reference spectralon, SpectrILight software. ILT900 works at wavelength 250 – 950 nm with 1 nm resolution. The field of view (FOV) of the sensor is 25°. To get optimal measurement result, data acquisition conducted following the principal suggested by Rundquist et al. [3]. The measurements were taken at 10 am to 2 pm local time, when the sun emits its maximum energy. The measurements conducted under direct sunlight of a clear sky or maximum 20% cloud. Fig 1 shows data acquisition configuration for spectral reflectance measurement of rice plantation. Four pre-processing steps were applied to the measured data before its ready to use i.e. data selection, averaging, smoothing, and spectral band removal. Data selection applied to remove anomalous samples based on spectral reflectance curve analysis. When the spectral reflectance curve of a particular sample tends flat for the low value or too high (close to 100%) then the sample will removed from training set. Since there are more than one measurement results from each plot in the survey area, we calculate the average of those results to generate single value for each plot. To get better signal to noise ratio (SNR), spectral reflectance value filtered with Savitzky-Golay filter. Several studies has been carried out to identify the factors that affect vegetation spectral reflectance e.g. pigments concentrations [4][5][6][7][8][9][10][11]. Based on those literatures, we chose the most optimal wavelength between 400 to 900 nm and remove the measurement results outside that range. Fig 2 shows spectral reflectance curve of rice variety IR78581-12-3-

Figure 1. Data acquisition configuration for spectral reflectance measurement

2-2 with 45 Kg/ha Nitrogen compositions at various growth stages. In addition to the spectral reflectance data, we also measured rice production data from each plot. We used this data as a reference for rice crop estimation process based on regression technique. The regression model takes spectral reflectance data as predictors and rice production data as response. After the optimal regression model generated, the optimal model will be use for rice crop estimation using MODIS data. Therefore, we selected only four spectral bands of ground-based data that match to MODIS spectral bands i.e. blue: 469.02 nm, green: 555.25 nm, red: 645.03 nm, and NIR1: 858.67 nm. C. MODIS Data Remote sensing technology provides a appropriate means to meet the requirement for regional-scale datasets of paddy rice fields. Several authors have studied the potential of Landsat and NOAA Advanced Very High Resolution Readiometer (AVHRR) data for paddy rice fields identification. A new generation of optical sensor such as MODIS, provides shortwave infrared bands that are sensitive to vegetation moisture and soil water [12]. MODIS sensor consists of 36 spectral bands; seven of those are designed for vegetation and land surfaces study: blue (459 – 479 nm), green (545 – 565 nm), red (620 – 670 nm), near infrared (NIR1: 841 – 875 nm, NIR2: 1230 – 1250 nm), and shortwave infrared (SWIR1: 1628 – 1652 nm, SWIR2: 2105 – 2155 nm). MODIS data can be downloaded for free from http://modis.gsfc.nasa.gov/. In this study we used MOD02QKM – Level 1B Calibrated Radiances data of Subang, West Java province, Indonesia. The study area is 3,128.81 hectares with total rice crop 12,362.281 ton. We have conducted survey in March 3, 2010, and collected the rice production of several spots (each 500 m x 500 m) in the study area. We focused on visible and near infrared wavelength of MODIS data, therefore only four spectral bands (blue, green, red, and NIR1) were used in the

product matrix S = XTY, thus including information on variation in both X and Y, and the correlation between them. PLSR aims to combine information on both X and Y in the definition of the scores and loadings. In theory, PLSR should have advantage over PCR. In a situation where a minor component in X is highly correlated with Y; insufficient component selection could lead to bad prediction result. In PLSR, such component would automatically in the first latent variable (LV). In most situations, the methods achieve similar prediction accuracies, although PLSR usually needs fever LV than PCR [13]. Figure 3. Study area

experiment. Some pre-processing steps were applied to the MODIS data i.e. georeferencing, crop the region of interest (ROI) area, and radiance to reflectance conversion. All preprocessing steps were conducted in ENVI software. Fig 3 shows the study area, yellow plots denotes center coordinates of surveyed spots. D. Principal Component Regression (PCR) PCR and PLSR are very popular in many fields since they have been designed to address common situation in natural science such as highly correlated predictor variables and relatively few samples. The most common example is in spectroscopy, where there are highly overlapping lines of spectra. The least square solution for 𝑌 = 𝑋𝐵 + 𝜀 is given by 𝐵 = (𝑋 𝑇 𝑋)−1 𝑋 𝑇 𝑌 to avoid the number of variables (columns) in X exceeds the number of objects (rows) and collinearities problem, PCR decompose X into orthogonal scores T and loadings P 𝑋 = 𝑇𝑃 and regressing Y on the first columns of the scores T. The scores are given by the left singular vectors of X, multiplied with the corresponding singular values. The loadings are the right side of singular vector of X. Algorithm: 1. Approximate the X matrix by the first a principal components (PCs), this can be obtained from the singular value decomposition (SVD): 𝑇 𝑇 𝑋 = 𝑋̃(𝑎) + 𝜀𝑋 = (𝑈(𝑎) 𝐷(𝑎) )𝑉(𝑎) + 𝜀𝑋 = 𝑇(𝑎) + 𝑃(𝑎) + 𝜀𝑋 2. Regress Y on the scores, which leads to regression coefficients 𝐵 = 𝑃(𝑇 𝑇 𝑇)−1 𝑇 𝑇 𝑌 = 𝑉𝐷 −1 𝑈 𝑇 𝑌 where the subscripts a have been dropped for clarity. The optimal number of first a components usually determined by cross-validation [13]. E. Partial Least Square Regression (PLSR) In PLSR, the components called Latent Variables or LVs, are obtained iteratively. Starts with the SVD of the cross

Algorithm: 1. The first left and right singular vectors, w and q, are used as weight vectors for X and Y, respectively to obtain scores t and u: 𝑡 = 𝑋𝑤 = 𝐸𝑤 𝑢 = 𝑌𝑞 = 𝐹𝑞 where E and F are initialized as X and Y, respectively. 2. The X scores t are often normalized: 𝑡 = 𝑡/√𝑡 𝑇 𝑡 the Y scores u are not necessary in the regression but are saved for interpretation purposes. 3. X and Y loadings are obtained by regressing against the same vector t: 𝑝 = 𝐸𝑇 𝑡 𝑞 = 𝐹𝑇 𝑡 4. Information to the latent variable, in the form of the outer products tpT and tqT, is subtracted from the data matrices E and F. 𝐸𝑛+1 = 𝐸𝑛 − 𝑡𝑝𝑇 𝐹𝑛+1 = 𝐹𝑛 − 𝑡𝑞 𝑇 5. The estimation of the next component then can start from 𝑇 𝑇 the SVD of the cross product matrix 𝐸𝑛+1 𝐹𝑛+1 . After every iteration, vector w, t, p, and q are saved as columns in matrices W, T, P and Q, respectively. It has been shown that an alternative way to represent the weights, in a way that all columns relate to the original X matrix, is given by 𝑅 = 𝑊(𝑃𝑇 𝑊)−1 6. Use scores T to calculate the regression coefficients, and later convert these back to the realm of the original variables by pre-multiplying with matrix R (since T = XR): 𝐵 = 𝑅(𝑇 𝑇 𝑇)−1 𝑇 𝑇 𝑌 = 𝑅𝑄𝑇 III. RESULT AND DISCUSSION To evaluate the regression model performance on rice crop estimation in real case, we conducted two experiments; first using ground-based data, second using MODIS data. The main objective of the first experiment is to evaluate regression model using ground-based data from ILT900 spectroradiometer. As explained in the section 2.A, the study area of the first experiment is in experimental farmland of the Ministry of Agriculture Republic of Indonesia in Sukamandi, Subang, West Java province, Indonesia. In the second experiment, we applied the model on MODIS data to see the regression model performance on satellite data.

A. Evaluation on ILT900 spectroradiometer Data The spectral reflectance data from ILT900 spectroradiometer used as independent variables and rice production as dependent variable. Table 1 and 2 shows analysis of variance (ANOVA) result of PCR and PLSR model. As can be seen on the table, r2 and root mean square error (RMSE) value of PLSR model are better than PCR model. TABLE 1. ANOVA OF PCR MODEL

Source

Degree of freedom

Sum of Squares

Model

4

1302.1881

Error

58

42.7207

Total

59

1344.9088

Mean Square

0.5101

YPCR = 2.575 + 0.715 X1 + 1.045 X2 + 0.864 X3 + 1.724 X4 r2 = 0.968 RMSE = 0.702

TABLE 2. ANOVA OF PLSR MODEL

Source

Degree of freedom

Sum of Squares

Model

4

1324.1199

Error

58

20.7888

Total

59

1344.9088

IV. CONCLUSIONS In this study we used ground-based data from ILT900 spectroradiometer combined with MOD02QKM – Level 1B Calibrated Radiances MODIS data used for rice crop estimation using PCR and PLSR model. PCR and PLSR model shows good rice crop estimation accuracy on groundbased data with r2 = 0.968 and 984 respectively. The estimated error on MODIS data could be minimized by applying radiometric correction in pre-processing steps. This correction step minimizes spectral reflectance variation from different date acquisitions. Further study will be the implementation of higher spatial resolution sensor for rice crop estimation. ACKNOWLEDGMENT This research was supported by Agency For The Assessment And Application Of Technology, Republic of Indonesia (BPPT). REFERENCES [1]

V. K. Dadhwal, R. P. Singh, S. Dutta, and J. S. Parihar, “Remote sensing based crop inventory: A review of Indian experience,” Trop. Ecol., vol. 43, no. 1, pp. 107–122, 2002.

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Mean Square

0.3647

YPCR = 2.575 + 0.715 X1 + 1.045 X2 + 0.864 X3 + 1.724 X4 r2 = 0.984 RMSE = 0.593

B. Evaluation on MODIS Data MODIS MOD02QKM – Level 1B Calibrated Radiances data of Subang, West Java province, Indonesia used as input data. After all pre-processing steps applied to the input data, we used spectral reflectance of four spectral bands (blue, green, red) as independent variables and rice production of several spots (500 m x 500 m) in study area as dependent variable. Table 3 shows the error estimation of PCR and PLSR model using MODIS data. Contrast to the previous experiment result, PLSR model shows higher estimated error than PCR model. Based on our previous study, the absence of radiometric correction might the cause of this fact. TABLE 3. ERROR ESTIMATION OF PCR AND PLSR MODELS

Model

Estimated error (%)

PCR

14.94

PLSR

23.87

[10] Y. J. Lee, C. M. Yang, K. W. Chang, and Y. Shen, “A simple spectral index using reflectance of 735 nm to assess nitrogen status of rice canopy,” Agron. J., vol. 100, pp. 205–212, 2008. [11] Y. Zhu, X. Yao, Y. Tian, X. Liu, and W. Cao, “Analysis of common canopy vegetation indices for indicating leaf nitrogen accumulations in wheat and rice,” Int. J. Appl. Earth Obs. Geoinf., vol. 10, no. 1, pp. 1–10, 2008. [12] X. Xiao, S. Boles, J. Liu, D. Zhuang, S. Frolking, C. Li, W. Salas, and B. Moore, “Mapping paddy rice agriculture in southern China using multitemporal MODIS images,” Remote Sens. Environ., vol. 95, pp. 480–492, 2005.

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Intelligent Kernel K-Means for Clustering Gene Expression Conference Paper (PDF Available) in Procedia Computer Science 59 · August 2015 with 138 Reads DOI: 10.1016/j.procs.2015.07.544 Conference: International Conference on Computer Science and Computational Intelligence 2015, At Binus University, Jakarta, Indonesia, Volume: 59

1st Teny Handhayani 1.72 · Tarumanagara University

2nd Lely Hiryanto 4.27 · Tarumanagara University

Abstract Intelligent Kernel K-Means is a fully unsupervised clustering algorithm based on kernel. It is able to cluster kernel matrix without any information regarding to the number of required clusters. Our experiment using gene expression of human colorectal carcinoma had shown that the genes were grouped into three clusters. Global silhouette value and davies-bouldin index of the resulted clusters indicated that they are trustworthy and compact. To analyze the relationship between the clustered genes and phenotypes of clinical data, we performed correlation (CR) between each of three phenotypes (distant metastasis, cancer and normal tissues, and lymph node) with genes in each cluster of original dataset and permuted dataset. The result of the correlation had shown that Cluster 1 and Cluster 2 of original dataset had signi_cantly higher CR than that of the permuted dataset. Among the three clusters, Cluster 3 contained smallest number of genes, but 16 out of 21 genes in that cluster were genes listed in Tumor Classi_er List (TCL).

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Intelligent Kernel K-Means for Clustering Gene Expression Teny Handhayania, Lely Hiryantob a b

Computer Science Department, Tarumanagara University, Jl. S. Parman No 1 Gedung R Lantai XI, Jakarta 11440, Indonesia Computer Science Department, Tarumanagara University, Jl. S. Parman No 1 Gedung R Lantai XI, Jakarta 11440, Indonesia

Abstract

Intelligent Kernel K-Means is a fully unsupervised clustering algorithm based on kernel. It is able to cluster kernel matrix without any information regarding to the number of required clusters. Our experiment using gene expression of human colorectal carcinoma had shown that the genes were grouped into three clusters. Global silhouette value and davies-bouldin index of the resulted clusters indicated that they are trustworthy and compact. To analyze the relationship between the clustered genes and phenotypes of clinical data, we performed correlation (CR) between each of three phenotypes (distant metastasis, cancer and normal tissues, and lymph node) with genes in each cluster of original dataset and permuted dataset. The result of the correlation had shown that Cluster 1 and Cluster 2 of original dataset had significantly higher CR than that of the permuted dataset. Among the three clusters, Cluster 3 contained smallest number of genes, but 16 out of 21 genes in that cluster were genes listed in Tumor Classifier List (TCL). Keywords:Kernel K-Means; Human Colorectal Carcinomal; Unsupervised Clustering Algorithm; Tumor Classifier List;

1. Introduction Biologists usually spend almost one year to analyze huge amount of newfound human genes that may contribute to cancer disease. In the field of Bioinformatics, there exist methods that could make the analysis time far more efficient. One of the methods is clustering, which can group genes based on their closest similarity. Hence, it can help find new genes having closest similarity with known highly suspected cancer genes. The Biologists can further analyze those new genes to confirm whether they contribute to cancer disease, but in far less amount of genes. Conventional clustering requires the number of clusters in advance, e.g. K-Means. In certain cases especially in clustering human colorectal carcinoma (cancer genes) [1]-[6], it can be difficult to define the number of clusters in advance, and fully unsupervised clustering is the solution [7][8]. Handhayani et. al [9] proposed a fully unsupervised clustering algorithm that combines Intelligent K-Means [7][8] and Kernel K-Means [10]. Intelligent K-Means can be implemented successfully for analyzing genes in colorectal carcinoma disease [11], but it only clusters data on input domain. Kernel K-Means [10], however, can be used for clustering kernel matrix but it still needs to know the number of clusters in advance. The aim of clustering kernel matrix is to analyze non-linearly separable data which most of the human gene expression are in that type of data. More significant information can be gained from Kernel Matrix. Current progress fulfils the first objective of our research, implementing the Intelligent Kernel K-Means proposed in [9] to cluster gene expression of colorectal carcinoma and calculate correlation ratio of the clustered genes with clinical data to further analyze the performance of the algorithm. 2. Intelligent Kernel K-Means Intelligent Kernel K-Means (IKKM) is a new clustering technique that can be used to cluster the data in the feature space. It is able to cluster kernel-based integration data of gene expression and DNA copy number [9]. Figure 1 depicts steps in fully unsupervised clustering gene expression of human colorectal carcinoma using IKKM into n-clusters. In the preprocessing step, we only used complete gene expression values of 111 tissues from 341 genes, as conducted by Muro et.al [12] and Ma’sum et.al [11]. Using those values, the kernel matrix is generated using Linear Kernel function of Equation (1). The generated kernel matrix is then fed into the clustering step using IKKM. Corresponding author: Email: [email protected], [email protected]

2

In the third step, firstly anomalous pattern algorithm is used to find centroid the first two centroids, as follows: 1. Compute Center of Mass (CoM) using Equation (2) from [10], 2. Find object C1 having the farthest distance from CoM using Equation (3) from [10], 3. Find another object C2 having the farthest distance from C1 using Equation (4) from [10], 4. Calculate distance of other objects against centroid C1 and C2. 5. Group the objects; Objects with distance closest to C1 are labelled as cluster S1, while the others with distance closes to C2 are labelled as cluster S2. Figure 2 shows illustration of those five steps for the first iteration of the algorithm. Next, Iterative Kernel Anomalous Pattern (see Figure 3 for illustration) is used to find other centroids, as follows: 1. For each cluster Si, find its new centroid candidate, , by finding the farthest object from its group centroid Ci using Equation (4), where i = 1,2, …,n-1. 2. Optimize distance among the centroids by finding the mean of distance value of each candidate, , with all group centroids, = mean(d(C1, ), d(C2, ), …, d(Cn-1, )) 3. The new centroid, Cn = max( , ,…, ). 4. Re-group all the objects according to their nearest distance with one of centroids {C1, C2, ..,Cn}; Hence, there may be cluster of {S1, S2, .., Sn}. 5. Repeat step 1 until there is no object change its cluster.

Fig. 1 Flowchart for Fully Unsupervised Clustering using Intelligent Kernel K-Means

Fig. 2 First Iteration of Anomalous Pattern Algorithm

3

Fig. 3 Next Iteration of Anomalous Pattern Algorithm

= . ∅( ) = ∑

(1)

∅( )

‖∅( ) − ∅ ‖ =

( , )+

(2)

∑,

,

‖∅( ) − ∅( )‖2 = ( , ) − 2 ( , ) + ( , ) Notes:      

− ∑

( ,

)

(3) (4)

K is kernel matrix X is gene expression data ∅( ) is center of mass l is size of kernel matrix x and z are vectors in kernel matrix i, j are index vector in kernel matrix.

3. Experimental Design We used dataset consisting of 1536 genes of human colorectal carcinoma, each gen consists of 111 tissues (100 cancer tissues and 11 normal tissues). After preprocessing, we only got 341 representative genes. Not only using the original dataset, we also generated permutated data from the dataset for analysis purpose. For evaluating the results of IKKM, we used the following techniques: 1. Cluster Visualization using scatter plot 2. Calculating global-silhouette[13, 6], and davies-bouldin index[14]. 3. Calculating correlation ratio using Equation (5) [12]

4

(

) ≡

∑

∑ ∈ ∑

⁄

, ,

̅

(5)

̅

Hence, nc is the number of genes in class Jc, xij is the expression level of gene i in sample j, is the average expression level of gene i. For correlation evaluation, we focused more on Cluster 1 and Cluster 2. Cluster 3 was evaluated separately due to its very small size (21 genes). Correlation was performed between each cluster and each of the three phenotypes, which are distant metastasis, tumor and normal tissue, lymph node metastasis. 4.

Result and Discussion

Using 341 genes as input, our algorithm produced 3 clusters. These clusters were then evaluated using the three evaluation techniques as shown in Figure 4, Table 1, and Figure 5 until Figure 7 respectively. We compared the results of IKKM with Intelligent K-Means (IKM) [11] using the first two evaluation techniques Figure 4 shows the scatter plots of clusters that produced by IKKM (Figure 4a) and IKM (Figure 4b). From the scatter plots, gene objects in the same color are in the same cluster. In Figure 4a, there are three colors, blue is for Cluster 1, green is Cluster 2, and red is Cluster 3. In Figure 4b, on the other hand, there are five colors, hence, five clusters. Comparing the two plots, IKKM can give better clustering. In each cluster of IKKM, we observed that almost all objects that are close to each other are in the same cluster.

Fig. 4 (a) Scatter plot of clusters IKKM (b) Scatter plot of clusters IKM

In terms of performance, IKKM gave better performance than IKM. Average silhouette of all IKKM clusters gave values > 0, meaning that the clusters were trustworthy, and it was also higher than that of IKM. IKKM clusters were also more compact, since it had half Davies - Bouldin index than that of IKM. Table 1 Global-Silhouette value, and Davies - Bouldin Index

The number of clusters Global Silhouette Davies - Bouldin index

Intelligent K-Means [11] 5 0.072468 2.2117

Intelligent Kernel K-Means 3 0.1453 1.0261

5

Fig. 5 Correlation of gene expression with distant metastasis phenotype. Vertical axis represents correlation ratio. (a) Genes cluster 1 (b) Genes cluster 2.

Fig. 6 Correlation of gene expression with tumor and normal tissue phenotype. Vertical axis represents correlation ratio. (a) Genes cluster 1 (b) Genes cluster 2.

Fig. 7 Correlation of gene expression with lymph node metastasis phenotype. Vertical axis represents correlation ratio. a) Genes cluster 1 (b) Genes cluster 2

We calculated a correlation ratio (CR) to serve as an indicator for correlation between each IKKM cluster with clinical data (distant metastasis, normal and cancer tissues, and lymph node metastasis phenotypes). In each cluster, genes were first sorted by CR value order, and then the CRs of the original dataset were compared with those of permuted dataset. The results of correlation analysis are shown in Figure 5, 6, and 7. The red line is original dataset and the blue lines are permuted dataset. For distant metastasis and normal and cancer tissues phenotypes, the CR values of Cluster 1 and Cluster 2 of original dataset were consistently higher throughout the full range of CRs,

6

suggesting that the correlation was global for each cluster. We could not identify significant correlation for the lymph node metastasis shown in Figure 7. Cluster 3 contains only a small number of genes, which were 21 genes. Thus, we analyzed it differently. In this cluster, 16 genes were in the Tumor Classifier List (TCL). Table 2 shows the 24 genes in TCL. Genes marked with * is the 16 genes which were also the member of Cluster 3. Table 3 shows the other 5 genes. We used the same analyzing method with the previous research [6] to show the correlation of genes in Cluster 3 with clinical data. We calculated the average gene expression of 16 representative genes, sorted them and split them into two groups. The first group was genes having positive average gene expression value, and the second group was the genes with the negative average of it. Figure 8 shows correlation of cluster 3 to the distant metastasis.

Fig. 8 Linkage of the clusters of expressed genes to the existence of distance metastasis in Cluster 3 Table 2 List of TCL (tumor-classifier) No GS Accession Symbol Annotation Number Number 1 GS3170 L35240 (*) Human enigma gene, complete coding sequence 2 GS2892 NM_004368 CNN2(*) Homo sapiens calponin 2 (CNN2), mRNA 3 GS4015 NM_005433 YES1(*) Homo sapiensv-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 (YES1), mRNA 4 GS4780 AD001530 (*) Homo sapiens XAP-5 mRNA, complete coding sequence 5 GS4941 NM_016380 (*) Homo sapiens differentiation-related protein dif13 (LOC51212), mRNA 6 GS4945 NM_016343 CENPF(*) Homo sapiens centromere protein F (350/400 kD, mitosin) (CENPF), mRNA 7 GS3387 NM_013317 hT1a-1(*) Homo sapienshT1a-1 (hT1a-1), mRNA 8 GS3386 NM_003337 UBE2B(*) Homo sapiens ubiquitin-conjugating enzyme E2B (RAD6 homolog) (UBE2B), mRNA 9 GS4946 NM_002439 MSH3(*) Homo sapiens mutS (E. coli) homolog 3 (MSH3), mRNA 10 GS3019 NM_003348 UBE2N(*) Homo sapiens ubiquitin-conjugating enzyme E2N (homologous to yeast UBC13) (UBE2N) 11 GS4022 NM_002433 MOG(*) Homo sapiens myelin oligodendrocyte glycoprotein (MOG), mRNA 12 GS715 AL096800 (*) Human DNA sequence from clone RP1-303A1 on chromosome 6 13 GS1102 Y18000 NF2(*) Homo sapiensNF2 gene 14 GS3002 AL023806 STM2(*) Human DNA sequence from clone 466P17 on chromosome 6q24 15 GS5239 AL139229 (*) Human DNA sequence from clone RP4-540A13 on chromosomeXq22.1-22.3 16 GS4163 AC007565 (*) Homo sapiens chromosome 19, cosmid R27656, complete sequence 17 GS3588 AF131848 Homo sapiens clone 24922 mRNA sequence, complete coding sequence 18 GS4947 NM_018520 Homo sapiens hypothetical protein PRO2268 (PRO2268), mRNA 19 GS1341 AC006165 Homo sapiens clone UWGC:y54c125 from 6p21, complete sequence 20 GS4512 NM_005768 C3F Homo sapiens putative protein similar to nessy(Drosophila) (C3F), mRNA 21 GS4501 AF261689 Homo sapiens DNA polymerase epsilon p17 subunit gene, complete coding sequence 22 GS6969 AL022316 Human DNA sequence from clone CTA-126B4 on chromosome 22q13.2-13.31 23 GS6493 AF113695 Homo sapiens clone FLB5224 PRO1365 mRNA, complete coding sequence 24 M15990 M15990 yes yes (*) Genes that found in Cluster 3 by Intelligent Kernel K-Means

7 Table 3 Addition genes in Cluster 3 No GS Accession Symbol Number Number 1 AA857163 AA857163 Amphiregulin 2 GS3339 U83246 3 GS3754 AF004429 4 GS3683 AK026017 5 GS3309 AB002135 GPAA1

Annotation Amphiregulin (schwannoma-derived growth factor) Homo sapiens copine I mRNA, complete cds. Homo sapiens D54 isoform (hD54) mRNA, partial cds. Homo sapiens cDNA: FLJ22364 fis, clone HRC06575. Homo sapiens mRNA for glycosylphosphatidylinositol anchor attachment 1 (GPAA1), complete cds.

5. Conclusion Intelligent Kernel K-Means (IKKM) is able to cluster the gene expression of human colorectal carcinoma into three trustworthy and compact clusters. Using the evaluation performance of calculating global-silhouette value and davied-bouldin index, the algorithm outperforms the Intelligent K-Means. Using clinical data of distant metastasis and human tissues, the Cluster 1 and Cluster 2 CR of original data indicates stronger relationship than that of permuted data. Although the number of genes in Cluster 3 is very small, 16 genes out of 21 genes are listed in TCL genes. In our next experiment, we will evaluate further the algorithm by using gene expression of breast cancer. Acknowledgements The first author says thank you to Muhammad Anwar Ma'sum for the meaningful and insightful discussion. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]

[12] [13] [14]

J.A. Berger, S. Hautaniemi, S.K. Mitra, J. Astola, Jointly Analyzing Gene Expression and Copy Number Data in Breast Cancer Using Data Reduction Models, IEEE/ACM Trans. Computational Biology and Bioinformatics,3(1) (2006) 2- 16. R.M. Neve et al, A Collection of breast cancer cell lines for study of functionally distict cancer subtypes, Journal of Cancer Cell, 10(6) (2006) 515-527. R.X. Manezez, M. Boetzer, M. Sieswerda, G-J. B. Ommen, J. M. Boer, Integrated analysis of DNA copy number and gene expression microarray data using gene sets, BMC Bioinformatics, 10(203), (2009) 1-15. C. Xu et al, Integrative analysis of DNA copy number and gene expression in metastatic oral squamous cell carcinoma identifies genes associated with poor survival, Molecular Cancer, 9(143) 1-12. J. Sheng, H.W. Deng, V.D. Calhoun,Y.P. Wang, Integrated Analysis of Gene Expression and Copy Number Data on Gene Shaving Using Independent Component Analysis, IEEE/ACM Trans. On Computational Biology and Bioinformatics, 8(6) (2011) 1568 – 1579. G. Wahyudi, I.Wasito, T.Melia, I.Budi, Robust consensus clustering for identification of expressed genes linked to malignancy of human colorectal carcinoma,6(7) (2011) 279-282. B. Mirkin, Clustering for Data Mining: A Data Recovery Approach, ChapmanandHall/CRC,Boca Raton,2005. M.M-T. Chiang, B.Mirkin, Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spread, Journal of Classification Springer,27 (2010) 3-4. T.Handhayani, I.Wasito, M.Sadikin, Ranny, Kernel Based Integration of Gene Expression and DNA Copy Number,in: Proc. of IEEE Advanced Computer Science and Information Systems (ICACSIS),2013,pp. 303 – 308. J.S-Taylor, N.Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, Cambridge,UK,2004. M. A. Ma'sum, I. Wasito, A. Nurhadiyatna, Intelligent K-Means Clustering For Expressed Genes Identification Linked to Malignancy of Human Colorectal Carcinoma,in: Proc. International Conference on Advanced Computer Science And Information System(ICACSIS), 2013, pp. 437-443. S. Muro et al, Identification of Expressed Genes Linked to Malignancy of Human Colorectal Carcinoma by Parametric Clustering of Quantitative Expression Data, Genome Biology,(2003). P. J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of Computational And Applied Mathematics,20 (1987) 53-65. D. Davies, D. Bouldin, A Cluster Separation Measure, " IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2) (1979).

7th

DISC 2015

Digital Information & Systems Conference

17 – 19 September 2015 “Menyongsong Masyarakat Ekonomi Asean Melalui Penguasaan Dalam Seni, Budaya, Pendidikan dan Teknologi”

Computer Engineering Dept. Faculty of Engineering UK. Maranatha Buku 2B ISBN : 978-979-1194-11-2

7th

DISC 2015

Digital Information & Systems Conference

17 - 19 September 2015 “Menyongsong Masyarakat Ekonomi Asean Melalui Penguasaan Dalam Seni, Budaya, Pendidikan dan Teknologi”

Buku 2B

Computer Engineering Dept. Faculty of Engineering UK. Maranatha ISBN: 978-979-1194-11-2

i

KATA PENGANTAR

Salam sejahtera. Merupakan anugerah yang tak terhingga bila kini seminar nasional DISC 2015 telah berjalan hingga yang ketujuh. Seminar nasional Digital Information and System Conference 2015 ini dapat terlaksana atas semua kerja keras panitia dan Himpunan Mahasiswa Sistem Komputer yang dengan tekun dan penuh semangat mewujudkan pelaksanaan seminar ini secara konsisten. Tentunya kami juga mengucapkan terima kasih atas partisipasi dari semua pemakalah dan peserta pada seminar DISC 2015 ini. Terutama pada para pemakalah yang selalu setia setiap tahunnya mengikuti seminar nasional DISC ini, kami sampaikan penghargaan dan ucapan terima kasih untuk semua dukungan dan partisipasinya tersebut. Melihat perkembangan situasi saat ini, ketika rakyat Indonesia harus memasuki era baru yaitu Masyarakat Ekonomi Asean yang kita kenal sebagai MEA, dan ini akan membuka paradigma yang sangat berbeda untuk menyingkapi masalah tersebut. Ketimpangan mutu pendidikan dan penelitian yang ada di Indonesia, bisa jadi merupakan sumber masalah yang akan merugikan bangsa dan negara Indonesia di bidang pendidikan dan penelitian pada saat MEA itu mulai berjalan. Karena hal tersebutlah maka pada DISC 2015 ini, diangkat sebuah tema yaitu: Menyongsong Masyarakat Ekonomi Asean Melalui Penguasaan Dalam Seni, Budaya, Pendidikan dan Teknologi. Sebuah topik yang terlihat sederhana dan mudah untuk melakukannya. Tapi sesungguhnya membutuhkan usaha dan kerja keras serta kerja sama di berbagai bidang yang tidak mudah untuk mewujudkannya. Saat ini kita perlu untuk melihat masa depan dengan semangat yang lebih baik dan konsisten, serta perencanaan terpadu dari pemerintah, dunia usaha dan pendidikan, untuk mewujudkannya. Atas dasar semangat inilah, kita mengadakan seminar DISC 2015 ini sebagai suatu wadah diskusi para peneliti Indonesia, pemaparan berbagai penemuan dan inovasi teknologi, seni budaya, sastra, pendidikan dan berbagai bidang lainnya yang dapat mengangkat derajat bangsa Indonesia menjadi lebih baik. Akhir kata, kami mengucapkan terima kasih untuk semua sumbangsih tenaga, waktu dan pemikirannya. Selamat berseminar, dan sukses untuk kita semua.

Bandung, September 2015.

Semuil Tjiharjadi Chairman of DISC 2015

ii

Daftar Isi Kata Pengantar

ii

Daftar isi

iii

Committee

vi

BUKU IIB PENGENDALIAN KESEIMBANGAN BALL AND PLATE PENGENDALI PID DAN PENGOLAHAN CITRA DIGITAL Rosalia H. Subrata, Universitas Trisakti Richard R. Ligianto, Universitas Trisakti

MENGGUNAKAN

1

PENGEMBANGAN MODEL PEMBELAJARANMATA KULIAH PASAR MODAL DENGAN METODE SIMULASI ONLINETRADING DI BURSA EFEK INDONESIA Sri Hermuningsih, Universitas Sarjanawiyata Tamansiswa Kristi Wardani, Universitas Sarjanawiyata Tamansiswa

7

MODEL MATURITAS PROSES PEMBELAJARAN SEBAGAI SARANA PENGEMBANGAN KUALITAS LULUSAN PERGURUAN TINGGI YANG BERKELANJUTAN J.Sudirwan, Bina Nusantara University Eka Miranda, Bina Nusantara University

12

PENGEMBANGAN KERANGKA KERJA PENJADWALAN SECARA REAL TIME BERBASIS SISTEM MULTI PADA JARINGAN CERDAS RAMAH LINGKUNGAN Anggoro Suryo Pramudyo, Universitas Sultan Ageng Tirtayasa Bobby Kurniawan2, Didik Aribowo, Universitas Sultan Ageng Tirtayasa

22

BEBAN AGEN

PENINGKATAN PROSES AUTHENTIKASI KEAMANAN JARINGAN (IP MUTIMEDIA SUBSYSTEM) PADA LTE (LONG TERM EVOLUTION) Dadiek Pranindito, ST3 Telkom

IMS

27

APLIKASI DEMPSTER SHAFER DALAM AKUISISI PENGETAHUAN SISTEM PAKAR UNTUK MENDIAGNOSIS PENYAKIT KEJIWAAN Chairisni Lubis, Universitas Tarumanagara Agus Budi Dharmawan, Universitas Tarumanagara Zyad Rusdi, Universitas Tarumanagara

33

APLIKASI MEDIA PEMBELAJARAN SISTEM & ANATOMI TUBUH MANUSIA MENGGUNAKAN TEKNOLOGI CLIENT SERVER BERBASIS ANDROID Wahyudi Setiawan, Universitas Trunojoyo Madura Farah Rahadiani, Universitas Trunojoyo Madura Budi Dwi Satoto, Universitas Trunojoyo Madura

39

iii

TELEMETRI SUHU MULTI INPUT SECARA REAL TIME DENGAN MEDIA WIRELESS MODEM YS-1020 BERBASIS ARDUINO DAN PENAMPIL GRAFIK PADA KOMPUTER Hidayat Nur Isnianto, Sekolah Vokasi UGM Imam Muttaqin, Sekolah Vokasi UGM Dwi Arianti, Sekolah Vokasi UGM

45

PENGEMBANGAN APLIKASI ENTERPRISE UNTUK TOKO PERABOT Gunawan, STMIK Mikroskil, Medan Fandi Halim, STMIK Mikroskil,Medan Erlinda, STMIK Mikroskil, Medan

52

SENSUALITAS KITSCH PADA IKLAN TV KOMERSIAL “BIJI SELASIH BINTANG TOEDJOE” Wildan Hanif, Institut Teknologi Bandung Yasraf Amir Piliang, Institut Teknologi Bandung Agung Eko Budi Waspada, Institut Teknologi Bandung

58

FOTOGRAFI SINEMATIK, SEBUAH PENDEKATAN GAYA FILM KE DALAM DUNIA FOTOGRAFI Wildan Hanif, Institut Teknologi Bandung

64

IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS DAN COMPONENT ANALYSIS PADA SISTEM AUTENTIKASI WAJAH Agus Budi Dharmawan, Universitas Tarumanagara Lina, Universitas Tarumanagara

69

INDEPENDENT

ALGORITMA DIFFERENTIAL EVOLUTION UNTUK PREDIKSI UMUR MATERIAL KOMPOSIT POLIMERIK DENGAN NEURAL NETWORKS Mas Irfan P. Hidayat, Institut Teknologi Sepuluh Nopember, Surabaya

LELAH

74

ECOMMERCE HANDPHONE DENGAN MODEL B2B, B2C, B2LELANG Muhammad Ali Syakur, Universitas Trunojoyo, Bangkalan

79

PERANCANGAN BAND PASS FILTER (BPF) FREKUENSI 3,5 GHz BERBASIS STUKTUR METAMATERIAL MIKROSTRIP Andri Suherman, Universitas Sultan Ageng Tirtayasa (UNTIRTA), Banten. Yus Rama Denny, Universitas Sultan Ageng Tirtayasa (UNTIRTA), Banten. Teguh Firmansyah, Universitas Sultan Ageng Tirtayasa (UNTIRTA), Banten. Toni, Sekolah Tinggi Penerbangan Indonesia (STPI). Curug. Tangerang. Banten

85

DESAIN ANTENA DUAL BAND MIKROSTRIP LINGKARAN UNTUK KOMUNIKASI NIRKABEL Teguh Firmansyah, Universitas Sultan Ageng Tirtayasa (UNTIRTA), Banten. Feti Fatonah, Sekolah Tinggi Penerbangan Indonesia (STPI). Curug. Tangerang. Banten

91

PERANCANGAN SISTEM INTEGRATED SMART STREET LIGHTING BERBASIS KOMUNIKASI WIRELESS ZIGBEE Yus Rama Denny, Universitas Sultan Ageng Tirtayasa (UNTIRTA), Banten. Teguh Firmansyah, Universitas Sultan Ageng Tirtayasa (UNTIRTA), Banten. Suhendar, Universitas Sultan Ageng Tirtayasa (UNTIRTA), Banten. Andika, Universitas Sultan Ageng Tirtayasa (UNTIRTA), Banten.

97

TRANSFORMASI NILAI PADA KAIN BATIK BAGI MASYARAKAT DI KABUPATEN TUBAN, KECAMATAN KEREK – JAWA TIMUR Fajar Ciptandi, Universitas Telkom Agus Sachari, Institut Teknologi Bandung Achmad Haldani, Institut Teknologi Bandung

103

iv

SENI RUPA PASEMAH POTENSI KONSEP SENI RUPA KHAS NUSANTARA A. Erwan Suryanegara, Institut Teknologi Bandung Dr. Agus Sachari , Institut Teknologi Bandung Prof. Dr. Tjetjep Rohendi Rohidi, M.A., Universitas Negeri Semarang

108

TRANSFORMASIPEMANFAATAN MATERIALDAN BENTUK BANGUNAN HUNI BALI AGA Ida Ayu Dyah Maharani, Institut Seni Indonesia Denpasar Imam Santosa, Institut Teknologi Bandung Prabu Wardono, Institut Teknologi Bandung

120

JALUR PERDAGANGAN INDUSTRI FURNITURE KAYU DI JAWA TENGAH SEBAGAI ELEMEN PENDUKUNG INDUSTRI KREATIF INDONESIA Arianti Ayu Puspita, M.Ds, Institut Teknologi Bandung Dr.Agus Sachari, Institut Teknologi Bandung Dr. Andar Bagus Sriwarno, Institut Teknologi Bandung

126

PENGARUH PARIWISATA PADA KEBERAGAMAN SENI RUPA SEBAGAI MODAL KULTURAL BALI (STUDI PADA KOMUNITAS DAN PERHELATAN SENI RUPA DI WILAYAH DENPASAR, KLUNGKUNG DAN SINGARAJA BALI) Willy Himawan, Institut Teknologi Bandung Setiawan Sabana, Institut Teknologi Bandung A. Rikrik Kusmara, Institut Teknologi Bandung

132

TRADISI MEMBATIK SEBAGAI BASIS PENGEMBANGAN DESA WISATA BATIK GIRILAYU Agus Nur Setyawan, Universitas Sebelas Maret Desy Nurcahyanti, Universitas Sebelas Maret Yayan Suherlan, Universitas Sebelas Maret

140

v

COMMITTEE KEPANITIAAN : 

Semuil Tjiharjadi ST. MM. MT.



Marvin Chandra Wijaya ST. MM. MT.



Andrew Sebastian Lehman, ST., M.Eng.



Rinto Yuniarso, S.Sos



Yayang Sriwati



Edi Wawan



Andrey



HIMA SISKOM

REVIEWER : 

Prof. Dr. Ir. Benjamin Soenarko. MSME. (ITB)



Dr. Bunamin Uning, ST., MT. (JD Edwards Consultant)



Dr. Ir. Ratna Dewi, ST., MT. (UK. Maranatha)



Dr. Yosafat A. P. ST. MT. (UK. Maranatha)



Ir. Widjono, M.Sc. (UK. Maranatha)



Semuil Tjiharjadi, ST., MM., MT. (UK. Maranatha)



Andrew Sebastian Lehman, ST., M.Eng. (UK. Maranatha)



Markus Tanubrata, ST., MM., MT. (UK. Maranatha)



Hendry Wong, ST., M.Kom. (UK. Maranatha)



Joan Nugroho, ST., MT., Ph.D (Cand) (National Central University – Taiwan)

vi

Digital Information & System Conference 2015 Universitas Kristen Maranatha

ISBN:978-979-1194-11-2

APLIKASI DEMPSTER SHAFER DALAM AKUISISI PENGETAHUAN SISTEM PAKAR UNTUK MENDIAGNOSIS PENYAKIT KEJIWAAN Chairisni Lubis 1) Agus Budi Dharmawan 2) Zyad Rusdi 3) Fakultas Teknologi Informasi Universitas Tarumanagara Jalan S.Parman No.1 Jakarta 11140 Indonesia [email protected]) [email protected]) [email protected])

ABSTRAK Sistem pakar adalah salah satu bagian dari ruang lingkup kecerdasan buatan (Artifical Intelligence) yang merupakan suatu sistem yang memiliki kemampuan untuk berpikir mendekati kepintaran seorang pakar dalam menjawab masalah-masalah yang dihadapi tanpa harus menghadirkan pakar pada saat itu juga. Domain yang dapat memanfaatkan aplikasi dari sistem pakar sebagai alat bantu dalam pelaksanaan kegiatan adalah bidang kedokteran atau medis. Pada penelitian ini, akan diteliti pemakaian metode Dempster Shaffer pada fakta berupa gejala penyakit kejiwaan yang akan digunakan sebagai input pada Sistem Pakar untuk mendiagnosis penyakit kejiwaan Skizofrenia. Tingkat kepercayaan adanya suatu gejala dalam suatu penyakit menggunakan teorema probabilitas. Tingkat keberhasilan diagnosis dengan menggunakan metode Dempster Shaffer untuk mengakuisisi pengetahuan pada penelitian ini sebesar 38.%. Hal ini disebabkan karena pada diagnosis penyakit kejiwaan Skizofrenia ini, gejala penyakit yang paling dominan yang sangat mempengaruhi hasil diagnosis, sedangkan metode Dempster Shaffer tetap memperhitungkan seluruh gejala. Kata kunci Akuisisi pengetahuan, Dempster Shaffer, Sistem Pakar, Skizofrenia 1.

Pendahuluan Sistem Pakar (Expert System) merupakan salah satu cabang Kecerdasan Buatan (Artificial Intelligent) dalam ilmu komputer yang dapat membantu manusia dalam melaksanakan kegiatannya. Sesuai dengan kecerdasan buatan yang bekerja berdasarkan pengetahuan yang diberikan, maka sistem pakar juga bekerja berdasarkan pengetahuan yang diberikan oleh seorang pakar. Salah satu komponen sistem pakar yang sangat penting adalah akuisisi pengetahuan (knowledge acquitition). Pada penelitian sebelumnya sistem pakar digunakan untuk mendiagnosis penyakit kejiwaan skizofrenia dengan menggunakan metode Certainty Factor dan Bayesian Probability untuk akuisisi pengetahuannya. Pengetahuannya didapat berdasarkan hasil diagnosis beberapa pakar. Hasil diagnosis yang dicapai dengan menggunakan metode Certainty Factor sebesar 88,75% dan Bayesian Probability sebesar 91.25%. Kesalahan diagnosis terjadi karena dipengaruhi oleh adanya irisan gejala pada semua penyakit. [1] Untuk meningkatkan hasil diagnosis sitem pakar untuk mendiagnosis penyakit kejiwaan skizofrenia, maka pada penelitian ini akan dicoba untuk mengaplikasikan metode Dempster Shafer untuk akuisisi pengetahuannya. Dempster Shafer bekerja berdasarkan kepercayaan dimana memungkinkan penggabungan kepercayaan sehingga dicapai suatu hasil yang memperhitungkan semua kejadian yang ada.

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2.

Landasan Teori Penelitian “ Aplikasi Dempster Shafer dalam Akuisisi Pengetahuan Sistem Pakar untuk Mendiagnosis Penyakit Kejiwaan “ ini merupakan Penelitian lanjutan dari penelitian sebelumnya yang berjudul “ Sistem Pakar untuk Mendiagnosis Penyakit Kejiwaan “. Pada penelitian ini metode Dempster Shaffer akan digunakan untuk meangakuisisi pengetahuan menggantikan metode Certainty Factor dan metode Bayesian Probability pada penelitian sebelumnya, karena belum didapatkan hasil diagnosis yang memuaskan. Pada bab ini akan dijelaskan teori mengenai metode Dempster Shaffer dan aplikasinya pada sistem pakar untuk mendeteksi penyakit kejiwaan Skizofrenia. 2.1. Teori Dempster Shafer Dempster Shafer adalah sebuah teori matematika berdasarkan kepercayaan dimana memungkinkan penggabungan kepercayaan sehingga dicapai suatu hasil yang memperhitungkan semua kejadian yang disediakan. Ada 3 hal mendasar dalam teori Dempster-Shaffer : – Basic probability assignment (bpa,m) – Belief function (Bel) – Plausibilty function (Pl)

Bpa merupakan bentuk dasar dalam teori pembuktian. Tingkat kepercayaan pada suatu bukti sama dengan massa/kumpulan dari obyek secara fisik. Kumpulan dari bukti/evidence akan mendukung tingkat kepercayaan. Ukuran terhadap adanya kumpulan bukti/evidence yang mendukung adanya kepercayaan dikenal dengan istilah bpa (basic probabilty assignment) atau kadang disimbolkan sebagai m. Perbedaan utama dari Dempster-Shaffer dengan teori probabilitas klasik adalah pada pertimbangan pada faktor pengabaian/ignorance. Setiap peluang dalam teori probablitas klasik harus didistribusikan secara merata. Pada teori dempster shafer dikenal dengan adanya frame of discernment atau environment yang dinotasikan dengan θ. Frame ini menunjukan semesta dari sekumpulan hipotesis. Tujuan teori dempster shafer sendiri adalah untuk mengaitkan ukuran kepercayaan elemen – elemen θ. Sehingga tidak semua evidence mendukung tiap- tiap elemen. Untuk itu diperlukan adanya probabilitas fungsi densitas (m). Nilai m bukan hanya mendefinisikan elemen – elemen θ saja, namun juga semua subsetnya. Andai diketahui X adalah subset dari θ, dengan m 1 sebagai funsi densitasnya, dan Y juga merupakan subset dari θ dengan m 2 sebagai fungsi densitasnya, maka dapat dibentuk fungsi kombinasi m 1 dan m2 yaitu m3, yaitu : [3]

m3 ( z ) =

å 1- å

xÇ y = z

m1 ( X ).m2 (Y )

x Ç y =Æ

m1 ( X ).m2 (Y ) (1)

Keterangan: m3(z) : nilai Kombinasi dari fungsi densitas dan m1(X) : Fungsi densitas terhadap X m2(Y) : Fungsi densitas terhadap Y 2.2 Aplikasi Dempster Shafer pada Sistem Pakar Dalam penelitian ini teori Dempster Shafer digunakan untuk mengakuisisi pengetahuan yang berasal dari hasil diagnosis penyakit kejiwaan Skizofrenia berdasarkan gejala-gejalanya. Pada sistem pakar ini, nilai setiap rule untuk penyakit skizofrenia ditentukan oleh fungsi densitas m. Setiap rule yang dibentuk berdasarkan data rekam medis menyatakan jenis penyakit skizofrenia berdasarkan gejala yang diderita. Nilai densitas m(H) merupakan nilai kemungkinan (probabilitas) jenis penyakit Skizofrenia (Hipotesis) berdasarkan gejala yang diderita (Evidence). Perhitungan

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probabilitas didapat dari peluang penyakit H jika munculnya gejala E dibagi dengan banyaknya penyakit H yang muncul, yaitu :

m(H)=

Kemungkinan hipotesis H muncul jika Evidence E terjadi Banyaknya H muncul

(2)

Jika terdapat gejala yang sama pada penyakit yang berbeda, maka perhitungan nilai densitas m dihitung berdasarkan persamaan (1). Contohnya, jika suatu gejala muncul pada 2 penyakit yang terdapat pada Rule 1 (R 1) dan Rule 2 (R2) maka fungsi densitas kombinasinya adalah : m(H1,H2) = [m(H1)+m(H2)]/2 2.3. Penyakit Kejiwaan Skizofrenia Pada penelitian ini, data penyakit kejiwaan yang digunakan masih sama seperti penelitian sebelumnya yaitu penyakit kejiwaan Skizofrenia yang digunakan untuk menggambarkan suatu gangguan psikiatrik mayor yang ditandai dengan adanya perubahan pada persepsi, pikiran, afek, dan perilaku seseorang. Kesadaran yang jernih dan kemampuan intelektual biasanya tetap terpelihara, walaupun deficit kognitif tertentu dapat berkembang kemudian (Sadock, 2003). Gangguan skizofrenik umumnya ditandai oleh distoris pikiran dan persepsi yang mendasar dan khas, dan oleh afek yang tidak wajar (Inappropriate) atau tumpul (blunted). Penyakit Skizofrenia terbagi menjadi 9 jenis yaitu: [4] 1. Skizofrenia Paranoid. 2. Skizofrenia Hebefrenik 3. Skizofrenia Katatonik. 4. Skizofrenia tak terinci (Undifferentiated) 5. Depresi Pasca-skizofrenia 6. Skizofrenia Residual 7. Skizofrenia Simpleks 8. Skizofrenia lainnya 9. Skizofrenia ytt. 3.

Pengujian dan Hasil Pengujian Pada penelitian “Aplikasi Dempster Shafer dalam Akuisisi Pengetahuan Sistem Pakar untuk Mendiagnosis Penyakit Kejiwaan “ ini, dibutuhkan suatu program aplikasi yang akan digunakan untuk mendiagnosis penyakit kejiwaan Skizofrenia. Program aplikasi dirancang berdasarkan struktur sistem pakar yang terdiri dari lingkungan pengembangan (Development Environment) dan lingkungan konsultasi (Development Consultation). Kedua diagram alur tersebut dapat dilihat pada gambar 1 dan gambar 2. Pada lingkungan pengembangan , input program berasal dari data rekam medik berupa gejala dan penyakit kejiwaan yang diperoleh dari Rumah Sakit Jiwa Dr.Soeharto Heerdjan. Rule yang akan dibentuk yang berisi jenis penyakit skizofrenia berdasarkan gejala yang diderita. Akuisisi pengetahuan akan dilakukan dengan menggunakan metode Dempster Shafer seperti yang dijelaskan pada bab 2. Pada lingkungan konsultasi, input program berupa gejala penyakit yang diderita pasien. Dengan menggunakan Inference Enggine, maka akan ditelusuri rule yang terpicu pada basis pengetahuan yang sudah terbentuk pada lingkungan pengembangan. Metode penelusuran yang digunakan adalah Forward Chaining, dimana penelusuran dimulai dari fakta (gejala penyakit) terlebih dahulu untuk menguji kebenaran hipotesis (jenis penyakit). Output dari lingkungan konsultasi berupa hasil diagnosis penyakit kejiwaan skizofrenia berdasarkan gejala penyakit yang diderita pasien.

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Lingkungan Pengembangan

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Lingkungan Konsultasi

Start

Start

Rekam Medis

Input Gejala

Akuisisi Pengetahuan

Mesin Inferensi

Basis Pengetahuan

Penggabungan nilai probabilitas dengan metode Dempster Shafer

Nilai probabilitas

Hasil Diagnosis

Rule disertai nilai probailitas

Stop

Stop

Gambar 1 Diagram Alur Lingkungan Pengembangan

Gambar 2 Diagram Alur Lingkungan Konsultasi

Tahap Pengujian dilakukan untuk membuktikan apakah hasil keluaran metode Dempster Shafer dalam Sistem Pakar untuk mendiagnosis penyakit kejiwaan Skizofrenia ini sesuai dengan fakta yang ada berupa hasil penelitian rekam medik pasien penyakit kejiwaan Skizofrenia (hasil diagnosis Pakar). Pembuatan basis data untuk aplikasi program ini dilakukan dengan melakukan penelitian terhadap data rekam medik penyakit kejiwaan Skizofreniadi RS Jiwa Dr. Soeharto Heerdjan Grogol. Jumlah data yang dihimpun sebanyak 331 data selama tahun 2009 sampai tahun 2013. Pengujian ini menggunakan 251 data untuk membuat rule yang dilakukan pada lingkungan pengembangan, serta untuk melakukan pengujian data training digunakan 16 data training dan untuk melakukan melakukan pengujian tahap testing digunakan 16 data testing yang dilakukan pada lingkungan konsultasi. 3.1 Hasil Pengujian Rule untuk diagnosis penyakit kejiwaan dibentuk pada lingkungan pengembangan. Untuk mengetahui unjuk kerja dari metode Dempster Shafer pada penelitian ini digunakan juga metode Certainty Factor (yang sudah dilakukan pada penelitian sebelumnya) untuk mendiagnosis penyakit kejiwaan ini. Bentuk pengujian yang digunakan pada lingkungan konsultasi adalah dengan membandingkan hasil diagnosis dari Dokter spesialis kejiwaan (Pakar) dengan hasil diagnosis sistem pakar ini. Pengujian dilakukan dengan mengisi Form yang berisi gejalagejala penyakit kejiwaan yang diderita pasien pada program Sistem Pakar yang dirancang seperti terlihat pada gambar 3. Hasil diagnosis penyakit kejiwaan berdasarkan gejala penyakit yang dimasukkan pada form gejala dapat dilihat pada form Hasil Konsultasi seperti yang terlihat pada gambar 4. Diagnosis dilakukan dengan menggunakan metode Dempster Shafer dan Certainty Factor seperti yang sudah dijelaskan pada bab sebelumnya. Hasil diagnosis kedua metode ini dibandingkan dengan hasil penelitian pakar (dokter kejiwaan) untuk menunjukkan tingkat keberhasilan kedua metode tersebut. Tingkat Keberhasilan Hasil Pengujian dengan menggunakan 16 data training dan 16 data testing dapat dilihat pada tabel 2 dan 3

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Gambar 3 Form Gejala Penyakit

Gambar 4 Form Hasil Konsultasi Tabel 2 Tingkat Keberhasilan data training NO.

Metode

Jumlah Kasus

Hasil Pengujian Benar

Salah

1.

Dempster Shafer

16

9

7

Tingkat Keberhasilan 56%

2.

Certainty Factor

16

14

2

88%

Tabel 3 Tingkat Keberhasilan data testing NO.

Metode

Jumlah Kasus

Hasil Pengujian Benar

Salah

1.

Dempster Shafer

16

6

10

Tingkat Keberhasilan 38%

2.

Certainty Factor

16

14

2

88%

3.2 Pembahasan Hasil Pengujian Metode Dempster Shafer mempertimbangkan seluruh rule yang terpicu berdasarkan gejala penyakit yang dimasukkan, sedangkan metode Certainty Factor dominan melihat nilai Kepastian terbesar dari kemunculan gejala. Dari hasil di atas terlihat diagnosis dengan menggunakan metoda Dempster Shafer lebih kecil daripada metode Certainty Factor. Hal ini dikarenakan data gejala yang diberikan dalam rekam medik adalah gejala yang dominan pada penyakit tersebut sehingga memiliki nilai Certainty Factor yang tinggi.

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Sebagai contoh pada data pengujian untuk penyakit Skizofrenia Simpleks, dimana salah satu gejala yang diinputkan adalah komunikasi lambat. Gejala tersebut memiliki nilai Certainty Factor yang tinggi yaitu sebesar 0.8681 sehingga penyakit Skizofrenia Simpleks terpilih dengan nilai kepercayaan 100%. Namun nilai Certainty Factor yang tinggi dapat juga membuat hasil yag dicapai oleh metode Certainty Factor menjadi kurang optimal ketika gejala yang dimasukkan justru memiliki nilai Certainty Factor yang lebih tinggi pada penyakit lain. Seperti contoh pada hasil Data Training pada penyakit Skizofrenia Tak Terinci. Gejala yang diinputkan adalah Teriak-teriak, curigaan, menggangu lingkungandan berbicara sendiri. Gejala-gejala tersebut memiliki nilai CF yang tinggi untuk penyakit selain Skizofrenia Tak terinci sehinggga menghasilkan hasil yang salah yaitu Paranoid 80% dan YTT 69.41% (tabel 2). Pada metode Demster Shafer nilai kepastian gejala yang tinggi tidak langsung memicu rule untuk memilih salah satu jenis penyakit namun tetap memperhitungkan seluruh nilai kepastian gejala yang lain. Sehingga pada hasil pengujian ini metode Dempster Shafer menghasilkan nilai tertinggi untuk penyakit Skizofrenia Tak Terinci. 4.

Kesimpulan Dalam Sistem Pakar untuk mendiagnosis penyakit Kejiwaan Skizofrenia ini metode Dempster Shafer masih memiliki tingkat keberhasilan diagnosis yang masih kecil yaitu 56 % untuk pengujian dengan menggunakan data training dan 38% untuk data testing. Metode Certainty Factor yang digunakan sebagai pembanding justru memiliki tingkat keberhasilan yang lebih tinggi yaitu 88% untuk pengujian dengan menggunakan data training dan 88% untuk data testing. Hal ini disebabkan karena pada diagnosis penyakit kejiwaan Skizofrenia ini, gejala penyakit yang paling dominan yang sangat mempengaruhi hasil diagnosis yang mendukung metode Certainty Factor sedangkan metode Dempster Shafer tetap memperhitungkan seluruh gejala.

REFERENSI [1] Chairisni Lubis, Agus Budi Dharmawan, Marthalisa dan Lucy Komala. Sistem Pakar untuk Mendiagnosis Penyakit kejiwaan Skizofrenia. Digital Information & Systems Conference, Buku 1A, Computer Engineering Dept, Faculty of Engineering, UK.Maranataha, Bandung. [2] Agus Budi Dharmawan, Chairisni Lubis, Christian Sugiarto. Perancangan Aplikasi Sistem Pakar untuk Mendiagnosis Penyakit Dalam dengan Metode Certainty Factor dan Dempster Shafer. Jurnal INFOTEK Dharma Putra Vol.9 No.1. April 2014. [3] Sri Kusumadewi, Artifical Intelligence (Teknik dan Aplikasinya), Graha Ilmu, Yogyakarta, 2003. [4] Departemen Kesehatan R.I Direktorat Jenderal Pelayanan Medik, S-PPDGJIII, 1995

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International Journal of Computer Theory and Engineering, Vol. 8, No. 1, February 2016

A Leukocyte Detection System Using Scale Invariant Feature Transform Method Lina Arlends Chris, Bagus Mulyawan, and Agus Budi Dharmawan 

and cropping, the system will recognize the leukocyte type using two different recognition methods: 1) the distance based recognition system using the Euclidean distance method, and 2) the color based recognition system using the co-occurrence matrix method. The remainder of this paper is organized as follows. In Section II, the proposed leukocyte detection system based on SIFT algorithmis explained. Section III presents the leukocyte recognition system, while Section IV describes the experimental setup and results. Finally, the conclusion is presented in Section V.

Abstract—This paper describes an automatic detection and recognition system of leukocytes on a given microscopic image. The developed system detects the locations of leukocytes from a blood cell image. After the automatic detection, the system classifies each leukocyte in one of the five categories (neutrophils, eosinophils, basophils, lymphocytes, and monocytes). The system processes an input image with the Scale Invariant Feature Transform (SIFT) algorithm for leukocyte detection. Meanwhile, two different recognition methods, i.e. the Euclidean distance and the Co-occurrence matrix methods are applied for automatic recognition. The combination of detection and recognition approaches provides the optimal recognition accuracies for almost all leukocyte types.

II. LEUKOCYTE DETECTION SYSTEM

Index Terms—Leukocyte detection, leukocyte recognition, microscopic image, scale invariant feature transform.

In the proposed system, the Scale Invariant Feature Transform (SIFT) method is applied to detect the leukocytes from the captured microscopic images. The SIFT algorithm, developed by Lowe [6]-[8] is an algorithm for image features generation which are invariant to image translation, scaling, rotation and partially invariant to illumination changes and affine projection [9]. The steps for defining the SIFT image features are as follows: 1) Scale space construction, 2) Keypoint localization, 3) Orientation assignment, 4) Keypoint descriptor. First, the system creates a scale space from the input imagesby calculating the Difference of Gaussian (DoG) using the Gaussian kernel. This step is necessary as an input image may consist unncessary details for detection or recognition processes. Therefore it is important to identify locations and scales that contains only the region of interest from an image. The first step for detecting locations that are invariant to scale changes is by constructing a continuous function of scale, known as the scale space. The scale space of an image is defined as a function, L(x, y, σ), that is produced from the convolution of a variable-scale Gaussian, G(x, y, σ), with an input image I(x, y)

I. INTRODUCTION Blood is a bodily fluid that delivers nutrients and oxygen to cells. The analysis of blood cells can be used to detect blood disorder or to determine the presence of infectious diseases in human body. In order to identify the hematopoietic system disorders, hematologists need to perform the blood cells identification and counting for every blood elements, such as the erythrocytes (red cells), leukocytes (white cells), and platelets [1]. Since the task is very tedious and really time consuming, an automaticblood detection, recognition, and counting system is necessary and helpful. Several researchers have proposed various methods todetect and recognize the blood cells, such as the work by Markiewicz using the Support Vector Machine method [2], Colunga with EMalgorithm [3], and Neural Network-based classifiers [4], [5]. However, the detection and recognition systems have not been tested for blood cells that were influenced by rotation or illumination effects after the segmentation process. In this paper, an automatic leukocyte detection system that can detect the white blood cell locations from microscopic images is developed. The proposed system works based on the Scale Invariant Feature Transform (SIFT) method. First, the system detects the white blood cells locations using the SIFT method, then the system crops the images which contains the region of interest. After the automatic detection

L( x, y, σ )  G( x, y, σ )  I ( x, y) where * is the convolution operation in x and y, and

G ( x, y, σ )  (1 2 2 )e ( x

2

 y 2 ) 2 2

(2)

To build the DoG pyramid, the input image is convolved iteratively with the Gaussian kernel. The last convolved image is down-sampled in each image direction by factor of 2, and the convolving process is repeated [9]. Each collection of images of the same size are then build together the so-called Gaussian pyramid, which is represented by a 3D function defined in Eq. 1. The DoG pyramid is computed from the difference of each two nearby images in Gaussian pyramid. Fig. 1 shows the constructed scale space images.

Manuscript received August 7, 2014; revised November 13, 2014. This work was supported in part by the Indonesian Directorate General of Higher Education under Hibah Penelitian Unggulan Perguruan Tinggi Tahun Anggaran 2014. Lina A. Chris, B. Mulyawan, and A. B. Dharmawan are with the Computer Science Department, Faculty of Information Technology, Tarumanagara University, Jl. Letjen. S. Parman 1, Jakarta 11440, Indonesia (e-mail: [email protected]).

DOI: 10.7763/IJCTE.2016.V8.1022

(1)

69


neighbors or smaller than all of them. To improve the stability of matching, the points that have low contrast or are poorly localized along an edge are rejected. After the thresholding step, the system performs the corner detection process. Harris corner detection algorithm is realized by calculating each pixel’s gradient [10]. If the absolute gradient values in two directions are both great, then judge the pixel as a corner. Harris corner detector is defined as follows [11]:

R  detM  k (traceM ) 2

(3)

 I u2 ( x, y)



I uv ( x, y)    2 I ( x , y ) I ( x , y ) v  uv  

 M ( x, y )  

I u2 ( x, y )  X 2   ( x, y )  I v2 ( x, y )  Y 2   ( x, y ) 

I uv ( x, y )  XY   ( x, y )

1   ( x, y )  e 2 where k is an empirical value;

x2  y 2 2 2



 ( x, y)

is a Gaussian

function; I u ( x, y ) and I v ( x, y ) are the partial derivatives of the gray scale in direction u and v at point

 , and

I uv ( x, y ) is the second-order mixed partial derivative; X and Y are the first-order directional differentials, which can be approximately calculated by convolving the gray scale and difference operators in direction u and v;  refers to convolution. If R exceeds certain threshold, then the point is determined as a corner. Once the SIFT feature location is determined, a main orientation is assigned to each feature based on local image gradients. For each pixel of the region around the feature location the gradient magnitude and orientation are computed respectively as [9]: m( x, y) 

Lx  1, y,    Lx  1, y,  2  Lx, y  1,    Lx, y  1,  2

Fig. 1. The scale space images for leukocyte detection system.

(7)  ( x, y)  arctanLx, y  1,    Lx, y  1,   / Lx  1, y,    Lx  1, y,  

The next step is to define keypoints. Keypoints are pixels from an image which have constant values for scaling, rotation, blurring, and illumination changes. Keypoint construction is done by finding the local extrema (maxima or minima) of DoG function. The local extrema are detected by comparing each pixel with its neighbors in the scale space. If the pixel value is higher or lower than the maxima or minima, then the pixel becomes the candidate for being a keypoint. The DoG function will have a strong response along edges, even if the location along the edge is poorly determined and therefore unstable to small amounts of noise. The next step is to localize keypoints. In keypoint localization, a threshold cutting is applied on simple contrast value for each keypoint. The low contrast feature points are generally less reliable than high contrast feature points. The keypoints are selected only if they are larger than all of these

(8) Finally, the region around a keypoint is divided into 4×4 boxes. The gradient magnitudes and orientations within each box are computed and weighted by appropriate Gaussian window, and the coordinate of each pixel and its gradient orientation are rotated relative to the keypoints orientation. Then, for each box an 8 bins orientation histogram is established. From the 16 obtained orientation histograms, a 128 dimensional vector (SIFT-descriptor) is built.

III. LEUKOCYTE RECOGNITION SYSTEM For the recognition system, two methods are applied to the system: 1) the distance based recognition system using the 70


Euclidean distance method, and 2) the color based recognition system using the co-occurrence matrix method. In the Euclidean distance based recognition system, the dissimilarities between the testing and training feature vectors are calculated using the Euclidean distance measurement [12], [13]: r = || x  w ||

Correlatio n 

f i, j  ..... f (i, j ) ..... ........ ......

xy

(13)

IV. EXPERIMENTS This section describes the experiments conducted for the proposed leukocyte detection and recognition system using the Scale Invariant Feature Transform method. We developed our own database, called the FTI-Untar blood cells database. The FTI-Untar blood cells database consists of a total of 183 blood cell images with 112 neutrophils images, 37 lymphocytes images, 21 monocytes images, 10eosinophils images, and 3 basophils images. The images were taken using a digital camera with 1600×1200 pixels that was attached to a microscope. Fig. 2 shows the sample of a blood cell image that is used in the experiment, whileFig. 3 shows the samples of blood cell images, the cropped leukocyte images, the cropped leukocyte images with 10% darken lighting effects, and the cropped leukocyte images with 10% brighten lighting effects.

(7)

With r is the Euclidean distance between x as the testing feature vector and w as the training feature vector. A small r value indicates a high similarity of two images. Meanwhile, in the color based recognition system using the co-occurrence matrix method, the co-occurrence matrix is constructed by clustering the gray-scale values of an image. Such matrix is derived from the angular relationship between the neighboring pixels as well as the distances between them. The higher the color intensity of an image, the larger size of co-occurrence matrix can be obtained. First, the probability value p(i, j) of the color frequency f(i, j) of index pair i and j is calculated by [12], [13]:

 f i, j  1 p (i, j )    f (i, j )  f (i, j )  ........

ii 0  jj 0 [(ij ) p(i, j )]  xy

(8)

Next, obtainthe Haralick features by processing the probability values of the co-occurrence matrix. Five characteristic features are processed in the proposed system, i.e. entropy, contrast, homogeneity, energy, and correlation. Entropy is used to measure the randomness of intensity distributions. The entropy value is calculated by: Fig. 2. The sample of a blood cell image for leukocyte detection system.

I

J

Entropy     pi, j log pi, j  i 0 j 0

(9)

We have conducted various experiments with various targets for the proposed leukocyte detection and recognition system. First, we evaluated the performance of the SIFT method for detecting the white blood cells. Table I shows the detection results using SIFT method. It is shown in Table I that the proposed system was able to detect white blood cells with more than 86.67% accuracy.

For taking into account the power of intensity differences in an image, the contrast characteristic is evaluated. The contrast value is calculated by: I

J

Contrast    i  j 2 pi, j  i 0 j 0

(10)

TABLE I: THE DETECTION RESULTS USING SIFT METHOD Blood Cell Σdata Σtraining Σ Accuracy Type testing (%) Neutrophil 112 67 45 86.67 Eosinophil 10 6 4 100 Basophil 3 2 1 100 Lymphocyte 37 22 15 86.67 Monocyte 21 12 9 100

The homogeneity which calculates the uniformity of intensity variations in an image, is the contrary of the image contrast. Below is the equation for calculating the homogeneity: pi, j  i 0 j 0 1  i  j I

J

Homogeneit y   

(11)

TABLE II: THE DETECTION RESULTS USING SIFT METHOD FOR DARKEN LIGHTING EFFECTS Blood Cell Σdata Accuracy (%) Type Natural Darken Darken Lighting 5% 10% Neutrophil 112 86.67 82.22 80 Eosinophil 10 100 100 75 Basophil 3 100 100 100 Lymphocyte 37 86.67 86.67 80 Monocyte 21 100 100 88.89

Next, energy, as the fourth features, is used to measure the texture uniformity. The energy value is calculated by: I

J

Energy    Pi, j 2 i 0 j 0

(12)

Finally, the correlation value is used to describe the relations between each pixel value with its neighbors. The correlation value is calculated by:

In the next experiments, we tested the system with various lighting conditions. Table II and Table III show the detection 71


results using SIFT method for images with various darken and brighten lighting conditions, respectively. The natural lighting means the condition where images were taken with microscope standard lighting, while darken and brighten effects were done by adjusting the contrast of the natural lighting images using a picture editing software. As shown in Table II, the detection accuracies for images with darken lighting effects were lower than that of with natural lighting. However, the detection accuracies were still higher than 75%. Meanwhile, the detection accuracies for images with brighten lighting effects were lower than that of the natural lighting and the darken effects. However, the detection accuracies were still higher than 75% as shown in Table III.

localizations in SIFT method. Table IV shows the detection results using SIFT method with three different threshold values: 0.02, 0.03, and 0.05. It is clearly seen that the detection accuracies using =0.03 gave the highest results compared to the other threshold values. Finally, we conducted experiments to recognize leukocyte types from images which have fixed window sizes, both for training and testing images, i.e. 47×47 pixels and 57×57 pixels. These dimensions were the average size of leukocyte cells that were captured from the microscopic images. The recognition accuracies for images with various cropping sizes are shown in Table V. We applied two recognition methods for the leukocyte recognition system: 1) the Euclidean Distance method and 2) the Co-occurrence Matrix method. Based on Table V, the overall recognition accuracies for images with 47×47 pixels and 57×57pixels sizes with the Euclidean Distance method were 82.79% and 84.93%, respectively. Meanwhile, the overall recognition accuracy for leukocyte recognition system using the Co-occurrence Matrix method was 75.93% for images with 47×47 pixels and 61.78% for images with 57×57pixels sizes. In general, for both recognition methods, it is clearly seen that Basophilwas the most difficult leukocyte type to find and to recognize.

TABLE III: THE DETECTION RESULTS USING SIFT METHOD FOR BRIGHTEN LIGHTING EFFECTS Blood Cell Σdata Accuracy (%) Type Natural Brighten Brighten Lighting 5% 10% Neutrophil Eosinophil Basophil Lymphocyte Monocyte

112 10 3 37 21

86.67 100 100 86.67 100

84.44 100 100 86.67 100

75.56 75 100 86.67 88.89

We also tested various threshold values for keypoint Leukocyte Type:

Neutrophil

Eosinophil

Basophil

Lymphocyte

Monocyte

Cropped leukocyte images Blood cell images with 10% brighten lighting effects Fig. 3. The image samples of white blood cells used in the experiments. Cropped leukocyte images

Cropped leukocyte images with 10% darken lighting effects

TABLE IV: THE DETECTION RESULTS USING SIFT METHOD WITH VARIOUS THRESHOLD VALUES FOR KEYPOINT LOCALIZATION Type Σdata Σtraining Σtesting Accuracy (%) =0.02 =0.03 =0.05 Neutrophil 112 67 45 77.78 86.67 93.33 Eosinophil 10 6 4 50 100 75 Basophil 3 2 1 0 100 100 Lymphocyte 37 22 15 66.67 93.33 93.33 Monocyte 21 12 9 88.89 100 100

72

International Journal of Computer Theory and Engineering, Vol. 8, No. 1, February 2016 TABLE V: THE RECOGNITION RESULTS FOR LEUKOCYTE IMAGES WITH VARIOUS CROPPING SIZES Accuracy (%) Image size 47×47 pixels Image size 57×57 pixels Σtraining Σtesting Euclidean Co-occurrence Euclidean Co-occurrence Distance Matrix Distance Matrix

Type Neutrophil Eosinophil Basophil Lymphocyte Monocyte

213 100 74 10 3

53 22 20 3 2

90.62 83.33 40 100 100

73.58 63.63 55 66.67 50

96.88 77.78 50 100 100

90.56 59.09 30 100 100

[12] L. A. Chris and B. Mulyawan, “A combination of feature selection and co-occurrence matrix methods for leukocyte recognition system,” Journal of Software Engineering and Applications, vol. 5, no. 12B, pp. 101-106, Dec. 2012. [13] A. Chris, S. Sugiharto, and Lina, “Detection of Abnormalities of Lymph Node Tissues using Image Texture Analysis,” in Proc. International Conference on Information Technology and Applied Mathematics, 2012, pp. 30-32.

V. CONCLUSION We have presented the Scale Invariant Feature Transform method to automatically detect leukocyte areas and recognize the leukocyte types from microscopic images. The detection results of the leukocyte images using SIFT method are highly dependent on the threshold value of keypoint localization. Other parameters such as lighting condition and window size also give significant effects on the accuracy of the system. For the recognition system, the Euclidean Distance method gives a slightly better result than that of the color based recognition, i.e. the Co-occurrence Matrix method. In the future, we consider to develop a dynamic window model for detecting the leukocyte area, the use of other color domains, i.e. Hue, Saturation, and Value (HSV) for improving the system’s accuracy.

Lina Arlends Chris was born in Padang, West Sumatera, Indonesia in 1979. She received a B.Eng. degree in computer science from Tarumanagara University, Indonesia in 2001, and a M.Sc. degree in computer science from the University of Indonesia in 2004. In 2005, she received the Japanese government scholarship as a research student at the Graduate School of Information Science, Nagoya University, Japan. Later she continued her graduate study and got her doctoral degree from Nagoya University, Japan in 2009. Currently she is working as a lecturer at Tarumanagara University, Indonesia. Her research interest is in pattern recognition and computer vision field. She is also a member of IEEE and IEICE.

REFERENCES L. A. Chris and B. Mulyawan, “Focused color intersection for leukocyte detection and recognition system,” International Journal of Information and Electronics Engineering, vol. 3, no. 5, pp. 498-501, Sep. 2013. [2] T. Markiewicz, “Data mining techniques for feature selection in blood cell recognition,” in Proc. European Symposium on Artificial Neural Networks, 2006, pp. 407-412. [3] M. C. Colunga, O. S. Siordia, and S. J. Maybank, “Leukocyte Recognition using EM-algorithm,” in Proc. 8th Mexican International Conference on Artificial Intelligence, 2009, pp. 545-555. [4] N. T. Umpon and P. D. Gader, “Training neural networks to count white blood cells via a minimum counting error objective function,” in Proc. International Conference on Pattern Recognition, 2000, pp. 2299-2302. [5] M. Beksac, M. S. Beksac, V. B. Tipi, H. A. Duru, M. U. Karakas, and A. Cakar, “An artificial intelligent diagnostic system on differential recognition of hematopoietic cells from microsopic images”, Cytometry, vol. 30, pp. 145-150, Jun. 1997. [6] D. G. Lowe, “Object Recognition from Local Scale-Invariant Features,” in Proc. International Conference on Computer Vision, 1999, pp. 1150-1157. [7] D. G. Lowe, “Local Feature View Clustering for 3D Object Recognition,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2001, pp. 682-688. [8] D. G. Lowe, “Distinctive image features from scale-invariant key-points,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, Feb. 2004. [9] F. Alhwarin, C. Wang, D. R-Durrant, A. Graser, “Improved SIFT-Features Matching for Object Recognition,” in Proc. BCS International Academic Conference, 2008, pp. 179-190. [10] C. Harris and M. Stephens, “A combined corner and edge detector,” in Proc. the 4th Alvey Vision Conference, 1988, pp. 147-151. [11] J. Chen, L-H. Zou, and L-H. Dou, “The comparison and application of corner detection algorithms,” Journal of Multimedia, vol. 4, no. 6, pp. 435-441, Dec. 2009.

[1]

Arlends Chris was born in Bogor, West Java, Indonesia on March 21, 1977. He got his medical doctor degree from the Faculty of Medicine from Tarumanagara University, Indonesia, in 2005. Later, he obtained his master degree in educational psychology from Tarumanagara University in 2009. Now, he is working as a lecturer at the Faculty of Medicine, Tarumanagara University. He is also a member of the Indonesian Medical Association.

Bagus Mulyawan was born in Yogyakarta, Central Java, Indonesia in 1962. He got his bachelor degree from the Computer Engineering Department, Gunadarma University, Indonesia in 1992. He obtained his master degree in information system management from Budi Luhur University, Jakarta, Indonesia in 2008. Now, he is working as a lecturer at the Faculty of Information Technology, Tarumanagara University.

Agus Budi Dharmawan received the MSc degree from the Department of Electrical Engineering, Fachbereich EIT, University of Applied Sciences Hochschule Darmstadt(hda) Germany in 2011 and the MT degree from the Department of Electrical Engineering, ITS Surabaya, Indonesia in 2011. He has been with the Tarumanagara University, Jakarta, Indonesia since 2008. His research interests include robotics, computer vision, machine learning, and pattern recognition

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A Leukocyte Detection System Using Scale Invariant Feature Transform Method LA Chris, B Mulyawan… - International Journal of …, 2016 - search.proquest.com Abstract This paper describes an automatic detection and recognition system of leukocytes on a given microscopic image. The developed system detects the locations of leukocytes from a blood cell image. After the automatic detection, the system classifies each ... Cited by 2 Related articles All 3 versions Cite Save

A Leukocyte Detection System Using Scale Invariant Feature Transform Method B Dharmawan - Proceedings of Second International Conference on …, 2016 - Springer Abstract This paper describes an automatic detection and recognition system of leukocytes on a given microscopic image. The developed system detects the locations of leukocytes from a blood cell image. After the automatic detection, the system classifies each ... Related articles Cite Save Showing the best results for this search. See all results

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