Proceedings of the

2012 International Conference on

System Engineering and Technology (ICSET)

11-12 September 2012 Bandung, Indonesia

Organizer: Co-organizer:

School of Electrical Engineering and Informatics Faculty of Electrical Engineering

Co-sponsored:

IEEE Computer Society - Indonesian Chapter IEEE Electron Devices, Education, Signal Processing, Power and Energy Systems Societies - Indonesian Joint Chapter

IEEE Catalog Number CFP1275P-ART ISBN 978-1-4673-2376-5

ICSET2012 COMMITTEES

Advisory Board Committee :

Prof.Dr.Suwarno (ITB) Dr.Gatot Hari Priowirjanto (SEAMOLEC) Associate Prof Ruhani Abdul Rahman (UiTM) Associate Prof. Dr. Wahidah Mansor (UiTM) Prof.Dr.Kuspriyanto (ITB) Prof.Dr.Bambang Riyanto (ITB) Prof.Dr.Carmadi Machbub (ITB) Dr.Ir. Iyas Munawar (ITB)

Program Committee :

Dr.Ir.Hilwadi Hindersah (ITB) Arief Syaichu Rohman Ir., MEngSc., PhD (ITB) Dr.Ir. Yoga Priyana (ITB) Dr.Ir. Agung Harsoyo (ITB) Dr.Ir. Emir Mauludi Husni (ITB) Prof. Dr. Mohd Nasir Taib (UiTM) Associate Prof. Md Mahfudz Md Zan (UiTM) Associate Prof. Dr. Noritawati Md Tahir (UiTM) Mohd Zahran Abdul Aziz (UiTM) Dr Rosidah Sam (UiTM) Associate Prof Rosni Abu Kassim (UiTM) Dr. Ramli Adnan (UiTM) Puan Juliana Johari (UiTM) Ir. Dr. Norlida Buniyamin (UiTM) Ith Vuthy (SEAMOLEC)

Organizing Committee :

General Chair :

Dr.Ir.Pranoto Hidaya Rusmin,MT (ITB)

Publications :

Dr-Tech Ary Setijadi Prihatmanto (ITB) Reza Darmakusuma,ST.,MT (ITB) Rifki Wijaya,S.Si.,MT (ITB) Riyanto Setiyono,ST.,MT (ITB)

Emi Iryanti,SST (ITB) Safreni Candra Sari,Ir.,MT (Delft) Secretary : Misa Maryam (ITB) Treasures : Dr.Ir.Aciek Ida wuryandari,MT (ITB) Yati Suyati (ITB) Local Program : Ferlin Ashadi (ITB) M.Vicky Ghani Aziz (ITB) Fuad Ughi (ITB)

PREFACE

Welcome to the ICSET 2012. International Conference on System Engineering and Technology is intended as a forum for researchers, academics, and practitioners to exchange theories, ideas, techniques and experiences related to all aspects of system engineering and technology as well as a platform to build or develop cooperative relationships among participants. The scope of the conference covers all fields in System Engineering and Technology, their methods, technologies, and applications.

ICSET 2012 is organized by Institut Teknologi Bandung (ITB), Indonesia and Universiti Teknologi Mara (UiTM), Malaysia. This conference is also sponsored by IEEE Computer Society Indonesian Chapter, IEEE Electron Devices Education Signal Processing and Power and Energy Systems Indonesia Joint Chapter.

This volume contains 101 papers that were carefully selected from 201 submissions for publication in the proceedings and oral presentation at the conference. The authors of submitted papers were across continents that consists of Indonesia, Malaysia, Palestine, Thailand, Taiwan, Bosnia and Herzegovina, United States of America, India, France, Cambodia, Iran, Netherlands and United Kingdom. In addition to regular presentations, three keynote speakers, i.e Ir.C. (Kees) Pronk (Delft University of Technology, NETHERLANDS), Dr. Ramli Adnan (Universiti Teknologi MARA, MALAYSIA), Arief Syaichu-Rohman, Ir., MEngSc., PhD (Institut Teknologi Bandung, INDONESIA) are invited to deliver lectures on System Engineering and Technology.

We are greatly in debt to many people and parties for their enthusiastic efforts that make this conference possible. Participation and supports of all authors, participants, committee members, secretariat, and sponsors are greatly appreciated. I thanks especially to reviewers for their precious expertise and timely reviews. Finally, We sincerely hope that all of the participants gain tremendous benefits while having fruitful and enjoyable experiences during the conference at ITB campus, Bandung, Indonesia.

Pranoto Hidaya Rusmin (ITB, Indonesia)

Chair of ICSET2012

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Design simulation and theoretical analysis of glucose sensingusing Polysilicon­based CMOS micromachined PiezoresistiveMicrocantilever

Nina Korlina Madzhi ; Sarah Addyani binti Shamsuddin ; NurulAkmal bin ZakariaPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (1468 KB) | HTML

Basic 3D interaction techniques in Augmented Reality

Yoki Ariyana ; Aciek Ida WuryandariPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (725 KB) | HTML

Autocatalytic set of chemical reactions of circulating fluidizedbed boiler

Sumarni Abu Bakar ; Noor Ainy Harish ; Fatin HazirahOsman ;Shafawati Ismail ; Siti Fudzla A'ini MokhtarPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (1)

| Abstract | PDF (334 KB) | HTML

Comparison between self­tuning Fuzzy­PID and Pole­Placement PID with application to saturated steam temperatureregulation

Ramli Adnan ; Mazidah Tajjudin ; Norlela Ishak ; HashimahIsmail ;Mohd Hezri Fazalul Rahiman ; Norhashim Mohd ArshadPublication Year: 2012, Page(s):1 ­ 5 Cited by: Papers (2)

| Abstract | PDF (661 KB) | HTML

Short­time Fourier Transform analysis of EEG signalgenerated during imagined writing

A. Zabidi ; W. Mansor ; Y. K. Lee ; C. W. N. F. Che Wan FadzalPublication Year: 2012, Page(s):1 ­ 4

| Abstract | PDF (269 KB) | HTML

E­mail system for Delay Tolerant Network

Emir Husni ; Agus WibowoPublication Year: 2012, Page(s):1 ­ 7 1 2

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| Abstract | PDF (476 KB) | HTML

Shopping application system with Near Field Communication(NFC) based on Android

Emir Husni ; Sugeng PurwantoroPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (1)

| Abstract | PDF (2657 KB) | HTML

Role of environmental factors in modeling of air temperatureelement in peninsular Malaysia

Fariza Yunus ; Jasmee Jaafar ; Zamalia Mahmud ; AzizShafie ;Roslina IdrisPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (285 KB) | HTML

Quantitative assessment of LiDAR dataset for topographicmaps revision

Roslina Idris ; Zulkiflee Abd Latif ; Jasmee Jaafar ; Nasri MohammadRani ; Fariza YunusPublication Year: 2012, Page(s):1 ­ 4

| Abstract | PDF (887 KB) | HTML

Investigation on effect of antenna positions to tag detectionpattern in RFIWS system

M. F. Saaid ; I. Ismail ; M. Z. H. NoorPublication Year: 2012, Page(s):1 ­ 4

| Abstract | PDF (496 KB) | HTML

Simulation of a LNG tanker track­keeping autopilot in canalwater

Agoes Priyanto ; Adi Maimun ; Rahimuddin ; Ang Yit SianPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (392 KB) | HTML

A study on qualitative assessment of LiDAR based digitalorthophotos generation

Norshafinaz Mohd Disa ; Khairil Afendy Hashim ; AnuarAhmad ;Abd. Manan Samad

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Publication Year: 2012, Page(s):1 ­ 4

| Abstract | PDF (1837 KB) | HTML

Multi­criteria selection for TNB transmission line route usingAHP and GIS

Faizah Husain ; Nur Aishah Sulaiman ; Khairil Afendy Hashim ;Abd.Manan SamadPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (1)

| Abstract | PDF (573 KB) | HTML

Generic PSV systems and their engine models

Armein Z. R. LangiPublication Year: 2012, Page(s):1 ­ 4 Cited by: Papers (5)

| Abstract | PDF (229 KB) | HTML

Longitudinal dynamic system modeling of a fixed­wing UAVtowards autonomous flight control system development: Acase study of BPPT wulung UAV platform

Fadjar R. Triputra ; Bambang R. Trilaksono ; Rianto A. Sasongko ;M.DahsyatPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (4)

| Abstract | PDF (390 KB) | HTML

Three, four transmit antennas space time block coded MIMO inRayleigh fading channel : Performance results

Subuh Pramono ; SugihartonoPublication Year: 2012, Page(s):1 ­ 4 Cited by: Papers (1)

| Abstract | PDF (245 KB) | HTML

Trusted platform for support services in cloud computingenvironment

Sneha Kolhe ; Sudhir DhagePublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (380 KB) | HTML

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Genetic algorithm for inter­frame region object temporalcorrelation in binary partition tree

Arief Setyanto ; John Charles Woods ; Mohammed GhanbariPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (395 KB) | HTML

Joint action optimation for robotic soccer multiagent usingreinforcement learning method

Safreni C. Sari ; Kuspriyanto ; Ary S. PrihatmantoPublication Year: 2012, Page(s):1 ­ 7

| Abstract | PDF (280 KB) | HTML

Decision system for robosoccer agent based on OODA Loop

Safreni C. Sari ; Kuspriyanto ; Ary S. PrihatmantoPublication Year: 2012, Page(s):1 ­ 7

| Abstract | PDF (677 KB) | HTML

Smart engineering using PSV­S concepts

Armein Z. R. LangiPublication Year: 2012, Page(s):1 ­ 4 Cited by: Papers (2)

| Abstract | PDF (227 KB) | HTML

The Analyze of Android's microphone audio streaming BeatME

Anna Ratnaningsih ; Aciek Ida Wuryandari ; Yoga PriyanaPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (365 KB) | HTML

The studies on Horizontal Coverage Pattern (HCP) for BusDetection Devices (BDD)

M. Z. H. Noor ; I. Ismail ; M. F. SaaidPublication Year: 2012, Page(s):1 ­ 4

| Abstract | PDF (945 KB) | HTML

Design and implementation of real time simulator withModelica

M. Hakim Adiprasetya ; Ary Setijadi Prihatmanto

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Publication Year: 2012, Page(s):1 ­ 7

| Abstract | PDF (686 KB) | HTML

Design and implementation multiple models of learning in 3Dsimulation game of nuclear application (SAN) (Case study :Diagnosis of coronary artery disease using Tc­Tetrofosmin)

Mira Rosalina ; W. Aciek Ida ; Tunggal MardionoPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (291 KB) | HTML

Design and implementation of the interface of simulation gameof nuclear application (SAN) (Case study: Diagnosis ofcoronary artery disease using Tc­Tetrofosmin)

Pratiwi Rahadiani ; W. Aciek Ida ; Tunggal MardionoPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (503 KB) | HTML

Design and implementation of interactive cyber exhibition onvirtual museum of Indonesia

Arief Syaichu Rohman ; Ary Setijadi Prihatmanto ; Ratri DwiKayungyunPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (1)

| Abstract | PDF (512 KB) | HTML

The design and analysis of the Space Exploration 3Dsimulation game

Rudi Kurniawan ; Arief Syaichu Rohman ; Emir Maulidi HusniPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (769 KB) | HTML

A second­order PSV­S and its performance model

Armein Z. R. LangiPublication Year: 2012, Page(s):1 ­ 4 Cited by: Papers (2)

| Abstract | PDF (222 KB) | HTML

Design and implementation character behavior of doctor andpatient in Coronary Heart Disease Diagnosis Game

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Using Tc­Tetrofosmin (SAN)

Sapdo Utomo ; Aciek Ida Wuryandari ; Tunggal MardionoPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (407 KB) | HTML

Design and implementation of BeatME Server for NetworkedMusical Performance

Sri Ariyani ; Aciek Ida Wuryandari ; Yoga PriyanaPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (348 KB) | HTML

Space Exploration 3D Game for classical learning in JuniorHigh School nine grade class

Widagdo ; Arief Syaichu Rohman ; Emir Maulidi HusniPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (202 KB) | HTML

The design and implementation discovery learning method onvirtual museum of Indonesia:(A case study museum of geologyfor rock materials)

Lia Laela Sarah ; Ary Setijadi Prihatmanto ; Pranoto Hidaya RusminPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (1020 KB) | HTML

Experimental study of extremum seeking control for maximumpower point tracking of PEM fuel cell

Muhammad Zakiyullah Romdlony ; Bambang RiyantoTrilaksono ;Romeo OrtegaPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (332 KB) | HTML

Model Predictive Control of hybrid fuelcell/battery/supercapacitor power sources

Amin ; Bambang Riyanto Trilaksono ; Arif Sasongko ; Arief SyaichuRohman ; Cees Jan Dronkers ; Romeo OrtegaPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (1)

| Abstract | PDF (496 KB) | HTML

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Histogram Equalization based on cumulative density function,Linear function, and Pixel position schemes for still image

B. Homnan ; W. BenjapolakulPublication Year: 2012, Page(s):1 ­ 7 Cited by: Papers (3)

| Abstract | PDF (251 KB) | HTML

Smartphone­based Pedestrian Dead Reckoning as an indoorpositioning system

Azkario Rizky Pratama ; Widyawan ; Risanuri HidayatPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (17)

| Abstract | PDF (393 KB) | HTML

Design and construction of remote­controlled quad­copterbased on STC12C5624AD

Stevie Jeremia ; Endrowednes Kuantama ; Julinda PangaribuanPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (907 KB) | HTML

The design and implementation determining age of fossilsgame simulation at virtual museum of Indonesia (A case studyat a Museum of Geology)

Yeni Nurhasanah ; Arief Syaichu Rohman ; Ary Setijadi PrihatmantoPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (486 KB) | HTML

Classification of forest change by integration of remotesensing data with Neural Network techniques

Ahmed A Mehdawi ; Baharin bin AhmadPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (775 KB) | HTML

Some framework, Architecture and Approach for analysis anetwork vulnerability

Tito Waluyo Purboyo ; Budi Rahardjo ; KuspriyantoPublication Year: 2012, Page(s):1 ­ 4

| Abstract | PDF (335 KB) | HTML

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Image contrast enhancement for film­based dental panoramicradiography

Suprijanto ; Gianto ; E. Juliastuti ; Azhari ; Lusi EpsilawatiPublication Year: 2012, Page(s):1 ­ 5 Cited by: Papers (2)

| Abstract | PDF (4362 KB) | HTML

Green microwave radiation absorbing paint

Hasnain Abdullah ; Dayang Suhaida Awang Damit ; Mohd NasirTaib ; Norhayati Mohamad Noor ; Wan Khairuddin Wan Ali ; Siti ZuraA. Jalil ; Ahmad Takiyuddin Abdullah ; Asmalia ZanalPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (238 KB) | HTML

Using Infrared Radiation to measure burger fat content

N. Buniyamin ; M. A. Mohd Shari ; M. H. Abdul Halim ; R. SamPublication Year: 2012, Page(s):1 ­ 5 Cited by: Papers (4)

| Abstract | PDF (584 KB) | HTML

Development of an integrated knowledge management andsimple process monitoring system

Norayu Mohamad ; Aisah Mohamed ; Zainuddin Mohamad ;NorlidaBuniyaminPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (706 KB) | HTML

Simulation of pick and place robotics system using SolidworksSoftmotion

Rosidah Sam ; Kamarul Arrifin ; Norlida BuniyaminPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (4)

| Abstract | PDF (954 KB) | HTML

The designing and implementation of a problem based learningin collaborative virtual environments using MMOG Technology

Ferdinand Aruan ; Ary Setijadi Prihatmanto ; HilwadiHindersah ;KuspriyantoPublication Year: 2012, Page(s):1 ­ 7

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| Abstract | PDF (3911 KB) | HTML

MEMS­based piezoelectric micropump for precise liquidhandling

Juliana Johari ; Burhanuddin Yeop MajlisPublication Year: 2012, Page(s):1 ­ 4 Cited by: Papers (1)

| Abstract | PDF (324 KB) | HTML

Analysis of human's brainwave pattern among active andinactive person

Rosni Abu Kassim ; Ahmad Shahran Ibrahim ; NorlidaBuniyamin ;Zunairah Hj MuratPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (582 KB) | HTML

A comparison on fish freshness determination method

Roziah Jarmin ; Lee Yoot Khuan ; Hadzli Hashim ; Nur HidayahAbdul RahmanPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (649 KB) | HTML

3D GIS system architecture for the aircraft simulation route ofSearch And Rescue operation

Beni Krisbiantoro ; Hilwadi Hindersah ; Tunggal MardionoPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (600 KB) | HTML

Gathering information realtime and anywhere (GIRA) using anANN algorithm

Aciek Ida Wuryandari ; Rifki WijayaPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (1441 KB) | HTML

Design and implementation of blood pressure measuring andoximetry (Android based)

Aciek Ida Wuryandari ; Herwin SuprijonoPublication Year: 2012, Page(s):1 ­ 6

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| Abstract | PDF (527 KB) | HTML

Preliminary design of smart healthcare system

Tunggal Mardiono ; Ary Setijadi ; Rifki WijayaPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (395 KB) | HTML

OpenSURF performance in windows phone 7

Rifki Wijaya ; Aciek Ida Wuryandari ; Haritz Cahya NugrahaPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (1415 KB) | HTML

Pattern recognition of finger movement detection usingSupport Vector Machine

Reza Darmakusuma ; Ary S. Prihatmanto ; Adi Indrayanto ; Tati L.MengkoPublication Year: 2012, Page(s):1 ­ 3

| Abstract | PDF (309 KB) | HTML

Design and implementation Infrared Guitar based on playingchords

Riyanto Setiyono ; Ary Setijadi Prihatmanto ; Pranoto HidayaRusminPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (400 KB) | HTML

Case study: Three dimensions biomedical visualisation on afull parallax hologram display

Aryo Wicaksono ; Ary Setijadi Prihatmanto ; Agung HarsoyoPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (271 KB) | HTML

Three dimensions medical CBCT reconstruction andvisualisation analysis

Aryo Wicaksono ; Agung Harsoyo ; Ary Setijadi PrihatmantoPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (389 KB) | HTML

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Prediction system of economic crisis in Indonesia using timeseries analysis and system dynamic optimized by geneticalgorithm

Siti Sa'adah ; The Houw Liong ; AdiwijayaPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (1)

| Abstract | PDF (309 KB) | HTML

Application of lung segmentation algorithm to diseasequantification from CT images

Nihad Mesanovic ; Svjetlana Mujagic ; Haris Huseinagic ; SamirKamenjakovicPublication Year: 2012, Page(s):1 ­ 7

| Abstract | PDF (485 KB) | HTML

Feature based iris recognition system functioning onextraction of 2D features

Gundeep Singh Bindra ; Arjun Agrawal ; Priyanka SharmaPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (226 KB) | HTML

Detection and removal of co­operative blackhole and grayholeattacks in MANETs

Gundeep Singh Bindra ; Ashish Kapoor ; Ashish Narang ; ArjunAgrawalPublication Year: 2012, Page(s):1 ­ 5 Cited by: Papers (3)

| Abstract | PDF (216 KB) | HTML

Evaluating energy drain rate of mobile nodes for effectiveroute lifetime in MANET

Gundeep Singh Bindra ; Prashant Kumar ; Krishen KantKandwal ;Seema KhannaPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (358 KB) | HTML

Implementation of gesture recognition on aquarium application

Adi Sucipto ; Agung Harsoyo ; Pranoto Hidaya RusminPublication Year: 2012, Page(s):1 ­ 4

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| Abstract | PDF (352 KB) | HTML

Face recognition based on auto­switching magnetic door locksystem using microcontroller

Harnani Hassan ; Raudah Abu Bakar ; Ahmad Thaqib FawwazMokhtarPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (4)

| Abstract | PDF (629 KB) | HTML

Investigation on perceptual and robustness of LSB digitalwatermarking scheme on Halal Logo authentication

Cik Ku Haroswati Che Ku Yahaya ; Harnani Hassan ; Mohd IzwanBin Md KahmiPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (1082 KB) | HTML

Smart home system for Disabled People via WirelessBluetooth

R. A. Ramlee ; D. H. Z. Tang ; M. M. IsmailPublication Year: 2012, Page(s):1 ­ 4 Cited by: Papers (7)

| Abstract | PDF (362 KB) | HTML

Identification of water/solid flow regime using ultrasonictomography

I. R. Muhamad ; Y. A. Wahab ; S. SaatPublication Year: 2012, Page(s):1 ­ 5

| Abstract | PDF (333 KB) | HTML

Design and implementation of BeatME as a Networked MusicPerformance (NMP) system

Randy Erfa Saputra ; Ary Setijadi PrihatmantoPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (492 KB) | HTML

Design and implementation of GIS data server development for3D simulation in SAR operation

Dimas Wicaksono ; Ary Setijadi Prihatmanto ; Tunggal Mardiono

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Publication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (421 KB) | HTML

Full design of a low­cost quadrotor UAV by student team

Jean­Baptiste Devaud ; Stéphane Najko ; Pierre Le Nahédic ;CédricMaussire ; Etienne Zante ; Julien MarzatPublication Year: 2012, Page(s):1 ­ 6 Cited by: Papers (3)

| Abstract | PDF (873 KB) | HTML

IT risk management framework based on ISO 31000:2009

Tati Ernawati ; Suhardi ; Doddi R. NugrohoPublication Year: 2012, Page(s):1 ­ 8 Cited by: Papers (2)

| Abstract | PDF (389 KB) | HTML

Design of Sliding Mode Control with observer based LinearMatrix Inequlity for input saturation system

Mirza Zoni ; Arief Syaichu RohmanPublication Year: 2012, Page(s):1 ­ 4

| Abstract | PDF (252 KB) | HTML

Design and implementation of architecture, classifier andintegrator for the Next Generation Urban Transportation U­Board

M. Vicky Ghani Aziz ; Ary Setijadi Prihatmanto ; Hilwadi HindersahPublication Year: 2012, Page(s):1 ­ 6

| Abstract | PDF (761 KB) | HTML

978-1-4673-2374-1/12/$31.00 ©2012 IEEE

2012 International Conference on System Engineering and Technology September 11-12, 2012, Bandung, Indonesia

Prediction System of Economic Crisis in Indonesia Using Time Series Analysis and System Dynamic

Optimized by Genetic Algorithm Siti Sa’adah#1, The Houw Liong*2, Adiwijaya#3

#1Informatic Engineering, #3Mathematic, #1Telkom Institute of Technology #3 Telkom Institute of Technology JL.Telekomunikasi No.1, Bandung, Indonesia

1tisataz@gmail.com 3adiwijaya@ittelkom.ac.id

*2Physic, Bandung Institute of Technology JL.Ganesha No.10, Bandung, Indonesia

2the@fi.itb.ac.id

Abstract — Economic crisis that had happened at 1997-1998 in Indonesia has stimulated researchers to study it further by utilizing economic indicators. The economic indicators, GDP (Gross Domestic Product) and inflation per year from 1980-2011, will be tested using time series analysis and system dynamic optimized by genetic algorithm. This research have applied system dynamic in order to get characteristic value of prediction economic crisis in Indonesia with various conditions besides genetic algorithm (GA) is used to help the dynamic system in finding a coefficient of data historic optimization.

The methods prior to predict consist of two phases, i.e. training and testing. The result shows 93% - 99% accuracy for training and up to 90% for testing. It concludes that the prediction system is able to fit data in finding historical optimal without avoid error.

Keywords — GDP, inflation, system dynamic, genetic algorithm, training, testing.

I. THE PROBLEM Every country has its own history of economic crisis. For

instance, Indonesia had bad economic crisis in 1998. It had debilitated so many important sectors. Economic sector is the heart of a country. When economic condition in a country is upset, all of sector will be annoyed. This crisis usually leads to catastrophic in economy. In order to mitigate the impact of the crisis, a system to predict how bad the effect can be should be developed. The system is aimed to be able to forecast the economic condition when crisis attack, so that governments, companies and civilian can have time to prepare themselves to deal with it.

Since 1999, studies regarding predictability of economic crisis have been carried out. There are three common models used as base for the development study currently. The first is signals approach. They are Kaminsky, Lizondo and Reinhart (1998), Kaminsky and Reinhart (1999), Goldstein, Kaminsky and Reinhart (2000), Alvarez-Plata and Schrooten (2004), Peng and Bajona (2008). The second model is parametric structural models, which were performed by Frankel and Rose (1996), Berg and Pattillo (1999), Kim and Moon (2001), Komulainen and Lukkarila (2003), Kumar, Moorthy and

Perraudin (2003), Beckmann, Menkhoff and Sawischlewski (2006), Kalotychou and Staikouras (2006) and Bussiere and Fratzscher (2006). The last model is techniques of computational intelligence like ones have been done by Kim, Oh, Sohn and Hwang (2004), Niemira and Saaty (2004), Kim, Hwang and Lee (2004), Pang and Feng (2006), Yu, Lai and Wang (2006), Celik and Karatepe (2007), and Sohn, Oh, Kim and Kim (2009).

Prior studies have succeeded in establishing a system as an economic crisis impact forecast. The studies are used to predict the effect based on each origin crisis causes without any internal correlation among them. As the growth of many sectors affected through economy, separated crisis causes will be no longer appropriate in implementing the forecast system. A further study to foresee the crisis impacts which encompass all of the crisis causes simultaneously should be developed. By incorporating all causes involved, the complexity and dynamics of real-world economic problems can be approached. The study required will comprise sophisticated analytical methods and techniques by applying data mining method. This paper is performed to develop a system in order to predict economic crisis in Indonesia by using time series analysis and system dynamic optimized by using genetic algorithm.

II. REVIEW OF LITERATURE AND STUDIES

A. Economic System in Indonesia Economic system is affected by many factors, such as

natural resources, which is reflected in Gross Domestic Product (GDP) value, and inflation. The two factors mentioned afore can be utilized as economic indicator in a country. Table 2.1 shows Indonesia economic indicator during 1997 – 2001.

1) GDP (Gross Domestic Product) GDP is total national income and outcome in relation to commodity and services in a period of time. GDP is one of main economic characteristic since it gives demonstration

of the economic activity. Higher value of GDP indicates better economic activity while lower value indicates the contrary. Expressed in billions of national currency units.

2) Inflation Inflation, also known as Costumer Price Index, is value that shows relation between goods price in domestic currency to foreign currency. Inflation can be said as a percentage increase in a price level. When the price of most goods and services continue to climb, it will lead to inflation. The inflation will eventually affect people economic condition [10]. Hence inflation can be used as parameter in this research.

B. Forecast of economic crisis in Indonesia Forecasting is a tool in predicting what will happen in

certain time in the future. In order to cover as many as possibilities occur, the certain time used is the time when the economic condition shows the deviation the most. This study selects Indonesia economic crisis in 1998 as the forecast base since it indicates the most unpredictable economic behavior for the past 20 years. The event of the prediction system is defined as the time when GDP is at its lowest point or and when inflation is at its highest point. This study predicts economic crisis for the next 1 or 2 years based on GDP and inflation data in 1980-2011.

Fig. 1 Influence of GDP and inflation toward of event short term economic crisis in Indonesia using data from 1980 – 2011

Fig. 1 shows that crisis indicator can be identified by outlier in the data time series. It signified the unusual data fluctuation data which is foretelling the crisis that might happen. Fig. 1 visualizes the condition of GDP and inflation in Indonesia during 1980 – 2011. It shows the unstable value of GDP and inflation in 1997 – 1998. As a matter of fact, Indonesia had suffered bad economic crisis in 1998 [9, 18].

C. Time Series Data Time series data is a sequential numeric variable which

have the characteristic in sequential of time. Time series data can be mathematically defined as follows.

(1) Where: t represents time in which xt is concerned.

D. System Dynamic System dynamic is related with the value of states in a

system over time [5]. It represents computer simulation modeling which used for identifying and managing complex feedback systems such as economic systems [18]. By utilizing system dynamic, the correlation between data indicator in predicting economic crisis can be noticed. This research has applied system dynamic in order to get characteristic value of prediction economic crisis in Indonesia with various conditions. The model dynamic developed as follows.

(2)

Where: represents data about economic indicator. The equation above can be approximated using the equation below.

(3) Where: = time difference ;

= coefficient in dynamic model; = ones of economic indicator; = 1, 2 ...; = 1, 2 …

E. Least square method Least square method is an approach to suit a mathematical

or statistical model. Henceforth the result will be used to fit the data in time series form and predict the system. Referred to equation (3), it can be approximated as the following.

(4) Where: = coefficient in dynamic model;

= ones of economic indicator; = 1, 2 …; = 1, 2 …

F. Genetic Algorithm Genetic algorithm (GA) is used to help the dynamic

system in finding a coefficient of data historic optimization. GA is an iterative calculation with a set of series, namely population, as a candidate solution with constant value. The population develops generation by generation through genetic operator application. During the iteration, namely generation, the structure of the population will be evacuated. It then will be selected as a basis for the next generation.

Every solution candidates have fitness value to show excess from others. Higher value fitness of individual shows its bigger opportunity in surviving and generating the descent. Recombination genetic material is simulated with operator genetic, such as reproduction, cross over and mutation. GA manipulates population in becoming potential solution in order to finish optimization problem.

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III. RESEARCH METHODOLOGY

A. Research Design In this research, the system is developed with the intention

to predict time series data. It is implemented in model dynamic by acquiring the least square error to find coefficient optimal using genetic algorithm. The time series data used here is GDP and inflation per year started from 1980-2011. It has been conducted in two main processes. The first is searching for the optimal pattern data history. The second is performing predicting a sequence data testing based on the pattern that had been found before. The process block diagram system of predicting economic crisis is shown as in Fig. 2.

Fig. 2 Block diagram economic crisis predict system

B. Population/Sampling The variance of variable used to obtain the best

performance prediction is specified based on the following scenario experiment. 1. Scenario 1:

• Using Number of Ra and Rb, -5 and 5, respectively. • Divider to the number of mutation: 100, 1000, 10000,

100000. • Number of maximum total individual: 2000.

2. Scenario 2: • Using Number of Ra and Rb: -10 and 10. • Divider to the number of mutation: 100, 1000, 10000,

100000. • Number of maximum total individual: 2000.

Where Ra is interval above and Rb is interval under.

The objective of both scenarios is to find the optimal pattern. The various values of Ra and Rb are estimated and used to find the huge space to GA searching the best fit coefficient. The range used is -5 to 5 and -10 to 10. It was selected based on trial and error of the phase testing code in GA, whereas the divider to the number from mutation value is to find the effect from mutation in the population.

Mutation is the most influence natural selection in GA other than recombination. The divider mutation also affected in finding best coefficient. The range of the divider is influenced by the range of the data. The interval divider number of mutation used are 100, 1000, 10000 and 100000.

C. Instrumentation and Data Collection Instrumentation and data collection are:

a. Data used in the system are data economic indicator per year taken from IMF website. They are GDP and inflation Indonesia from 1980-2011. The total data used is 30 data.

b. Clustering of data training and data testing is using the scenario experiment as follows

- Data Training : 22 year (1980 - 2000) - Data Testing : 10 year (2000 - 2011)

D. Tools for Data Analysis The calculation of accuracy used the equation below.

(5)

MAPE (Mean Absolute Percentage Error) is utilized to calculate the error between prediction and actual data. The equation of MAPE is as the following. (6)

Where: = Total of prediction data. = data actual. = data result of predict.

IV. PRESENTATION, ANALYSIS AND INTERPRETATION OF DATA

This chapter provides some result of prediction system. A. Presentation and Analysis of Data

The best result of the scenario 1 and 2 are explained as follows.

The content of the scenario 1 are Ra = -5 and Rb = 5, divider to the number of mutation = 10000, number of maximum total individual = 2000. The best results in scenario 1 are summarized from Fig. 3 to Fig. 6.

Start

Data Actual Parameter Model

GA in Least Square Method

Result Data Normalized

Normalized Data

Test The Coefficient Optimal Pattern with

Accuracy

Find Data Optimal Pattern

Data Result of Predict

Renormalized End

( ) %1001 ∗−= MAPEaccuracy

∑=

−=N

I ZZZ

NMAPE

1

*1

Z*Z

N

Fig. 3 The best result scenario 1 facing data train of gdfrom 1980 - 2001

Fig. 4 The best result scenario 1 facing data train ofrom 1980 - 2001

Fig. 5 The best result scenario 1 facing data test of gdpfrom 2002 - 2011

Fig. 6 The best result scenario 1 facing data test of inflfrom 2002 - 2011

The accuracy result of training phase is and 98.9% for GDP. The error in this systedependencies of inflation and GDP. In thithe influencer. Fig.3 and Fig.5 shows that value of GDP is minus while the values of

-20

-10

0

10

20

1980198219841986198819901992199419961998200020022004

GDP

(

%)

Year from 1980 - 2001

-40-20

020406080

19781980198219841986198819901992199419961998200020022004

Inflatio

n

(

%)

Year from 1980 - 2001

-20

-15

-10

-5

0

5

10

2000 2005 2010 2015

GDP

(

%)

Year from 2002 - 2011

-30

-20

-10

0

10

20

2000 2005 2010 2015

Inflatio

n

(

%)

Year from 2002 - 2011

dp in indonesia

of inflation in indonesia

p in indonesia

ation in indonesia

99.9% for inflation em is caused by the is case, inflation is

the first predicted actual GDP data is

+20. It meets the characteristic stimulus of economic crisis is tof rupiah to dollar.

The testing of GDP and inFig.6. The accuracy result ofinflation and 80,5% for GDP. the data used in test was toocomplex and chaos so that consequence, the system cannoresult of percent accuracy summ

TABRESULT OF THE BEST TRAIN AND TE

Train

Accuracy (%)

GDP Inflatio

98,940 99,965

Fig. 7 The best result scenario 1 facinand gdp in indonesia from 1980 – 2011

The system predicted that

inflation is 14.79 and 40.48, repattern that had been read in thas the influencer. But in result2013, the value of GDP is -8,55is shown in Table 3. Both fromthat economic is chaos and population are Ra = -5 and Rb =scenario 1 the predict system dibecause usually, crisis occurredinflation is complement.

The content of the scenaridivider to the number of mumaximum total individual = 2002 are summarized from Fig. 8 to

TABL

RESULT OF SHORT TERM PRE

Year GDP Predic

2012 14,794

2013 -8,557

GD

Inflation

Inflation Train

GD…

Inflation

Inflation Test

economic in Indonesia that the the rapid fall in exchange rates

nflation is shown in Fig.5 and f testing phase is 93,8% for The accuracy is smaller since

o small. Economic system is error is undeniable. As a

ot find the optimal pattern. The marized in Table 2.

LE I ST IN SCENARIO 1 USING ACCURACY

Test

on GDP Inflation

5 80,558 93,807

ng data predict of learn about inflation

in 2012 the value of GDP and espectively. This caused by the his situation when the inflation t in 2013 shows the others. In 57 yet the inflation is -37.78. It m the prediction result showed

complex theory using area = 5. It can be concluded, using id not give the optimal pattern, d when the value of GDP and

io 2 are Ra = -10 and Rb = 10, utation = 10000, number of 00. The best results in scenario o Fig. 11.

LE III EDICTION USING SCENARIO 1

ct Inflation Predict

40,480

-37,788

Fig. 8 The best result scenario 2 facing data train of gdp in indonesia from 1980 - 2001

Fig. 9 The best result scenario 2 facing data train of inflation in indonesia from 1980 - 2001

Fig. 10 The best result scenario 2 facing data test of inflation in indonesia from 2002 - 2011

Fig. 11 The best result scenario 2 facing data test of gdp in indonesia from 2002 - 2011

TABLE IIIII RESULT OF THE BEST TRAIN AND TEST IN SCENARIO 2 USING ACCURACY

Train Test

Accuracy (%)

GDP Inflation GDP Inflation

93,372 97,594 99,543 98,929

Due to allowable error tolerance in accuracy result in training and testing phase, the system can hopefully valid enough predict economic crisis 1 – 2 years ahead by learning pattern from data predict of learn as visualized in Fig. 12. This scenario gives better result than scenario 1. By applying Ra = -10 and Rb = 10, best coefficient with the smallest error can be achieved with the real value in billion. It is proved by the accuracy in process training is 93,37% for GDP and 97,59% for inflation. Table IV shows the accuracy result of training in scenario 2.

Fig. 12 The best result scenario 2 facing data predict of learn about inflation and gdp in indonesia from 1980 – 2011

TABLE IVV

RESULT OF SHORT TERM PREDICTION USING SCENARIO 2

Year GDP Predict Inflation Predict

2012 18,771 33,522

2013 -17,679 162,970

Fig. 12 indicates that the system predicted is able to find a sign of crisis. The system prediction using GDP and inflation data shows the gradually increment after sudden decreasing as the actual data crisis in 1998. The product is the crisis will occur.

From both scenarios, it can be said that when prediction system of economic crisis optimized by genetic algorithm in finding data, is given by natural selection, namely cross over and mutation. By them, it will impact new generation with the best individual in population. So, if the parents are the best individuals, then the child could be the best individual of the next parents. Finally, the last population with the best individual will give the best coefficient which has the small error in fitting data. After that the results of train and test are measure using accuracy.

B. Interpretation of Data As the figure result of scenario 1 and 2, GDP and inflation

have causal effect due to the correlation of one another. Nevertheless the system still has error in predicting. The increment of inflation automatically gives effect on GDP.

-40

-20

0

20

19781980198219841986198819901992199419961998200020022004

GDP

(

%)

Year from 1980 - 2001

GG

-50

0

50

100

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Infla

t

ion

(

%)

Year from 1980 - 2001

Inflation

Inflation Train

0

5

10

15

200020052010 2015

(

%)

Year from 2002 - 2011

Inflation

Inflation Test

02468

2000 2005 2010 2015

(%)

Year from 2002 - 2012

GDP

GDP Test

-40

-20

0

20

40

60

80

1980198219841986198819901992199419961998200020022004200620082010(

%)

Year from 1980 - 2011

GDP

GDP Learn Predict

Inflation

Inflation Learn Predict

C. Summary of Findings The result indicates that economic is a complex and

chaotic systems though data training and data testing give accuracy around 90%, where the real value is in billion. It can be seen from the result of the scenarios that the dynamic of the economic indicator is interdependently so that the error cannot be reduced.

V. CONCLUSION

A. Conclusions The conclusions generated based on this research are

summarized as follows. • In training phase, the prediction system of economic crisis

gives the accuracy 99%. It is caused by the system able to find best coefficient in fitting data with the error about 1%, with the real value in million rupiah.

• In testing phase, the prediction system economic crisis gives the accuracy 80 - 90%. It is caused by the small range of data used causing the data did not have pattern to be optimally identified. It affected in finding the best coefficient before failed to fit the data with a small error.

• Prediction result can be said not good enough to be used to predict economic crisis originally. Economic system is a complex and chaos system. Complex system is a system which concern to other elements. Whereas, chaos is system when in the first step still get a small error, but after that the error will be hard to be reduced.

• The data used as economic indicator gives big impact the result. It is proved from the result of both scenarios in which the indicator is dependent each other.

• The changes of parameter in genetic algorithm also give impact to the result. They are Ra and Rb, divider number of mutation, and maximum total individual. Ra and Rb were used for range of coefficient result whereas the divider number of mutation was for process mutation. The maximum total individual for the iteration maximum individual in population.

B. Recommendations The recommendations proposed based on this research are

summarized as follows. • The predict system economic crisis has to use many more

data economic indicator as variables input. • Using the data of economic indicator per month. • The government has to change the policy of monetary and

politics so that the economic crisis is not influenced by them.

• Other algorithm optimization needs to be carried out, as comparison, in finding coefficient optimal for predicting system economic crisis with the intention of better result.

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