PENERAPAN METODE NEURAL NETWORK UNTUK MEMPREDIKSI HASIL PEMILU LEGISLATIF
Keywords:
Data Mining, Neural Networks, Prediction of Legislative Elections
Abstract
Pemilihan Umum di Indonesia telah mengalami beberapa perubahan dari periode Pemilu ke periode Pemilu yang lain. Selama pemilu Orde Baru, kita mengenal sistem pemilu proporsional dengan daftar tertutup. Keterpilihan calon legislatif bukan ditentukan pemilih, melainkan menjadi kewenangan elite partai politik sesuai dengan susunan daftar caleg beserta nomor urut. Dalam sistem demikian, kedudukan parpol menjadi sangat kuat terhadap kadernya di parlemen. Namun di satu sisi, basis sosial dan relasi politik para wakil rakyat dengan konstituen menjadi lemah. Inilah yang menyebabkan kedudukan caleg terpilih mereka menjadi ”jauh” dalam hubungannya dengan konstituen. Semangat memilih langsung wakil rakyat baru mulai diakomodasi pada Pemilu 2004 melalui UU No. 12 Tahun 2003, dengan menggunakan sistem proporsional dengan daftar calon terbuka. Pemilih tidak hanya memilih tanda gambar parpol, tetapi juga diberi kesempatan memilih caleg.Penelitian yang berhubungan dengan pemilu sudah pernah dilakukan oleh peneliti yaitu dengan menggunakan metode decision tree dan classification tree dan estimator bayesian. Pada penelitian ini peneliti akan menggunakan metode neural network. Neural network telah menunjukkan hasil yang menjanjikan dalam prediksi untuk data time-series dibandingkan dengan pendekatan tradisional sehingga hasil prediksi pemilu legislatif DKI Jakarta lebih akurat
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Gill, G. S. , 2005, Election Result Forecasting Using two layer Perceptron Network. Journal of Theoritical and Applied Information Technology Volume.4 No.11 , 144-146.
Gorunescu, F. , 2011, Data Mining Concept Model Technique. India: Springer.
Gray, D. E. , 2004, Doing Research in the Real World. New Delhi: SAGE.
Han, J., & Kamber, M. , 2007, Data Mining Concepts and Technique. Morgan Kaufmann publisher.
K, G. S., & Deepa, D. S. , 2011, Analysis of Computing Algorithm using Momentum in Neural Networks. Journal of computing, volume 3, issue 6 , 163-166.
Kothari, C. R. , 2004, Research Methodology Methods and Technique. India: New Age International.
Kusrini, & Luthfi, E. T. , 2009, Algoritma Data mining. Yogyakarta: Andi.
Larose, D. T. , 2005, Discovering Knowledge in Data. Canada: Wiley Interscience.
Ling, S. H., Nguyen, H. T., & Chan, K. Y. , 2009, A New Particle Swarm Optimization Algorithm for Neural Network Optimization. Network and System Security, third International Conference , 516-521.
Maimon, O., & Rokach, L. , 2010, Data Mining and Knowledge Discovery Handbook. London: Springer.
Moscato, P., Mathieson, L., Mendes, A., & Berreta, R. , 2005, The Electronic Primaries: Prediction The U.S. Presidential Using Feature Selection with safe data. ACSC '05 Proceeding of the twenty-eighth Australian conference on Computer Science Volume 38 , 371-379.
Min Qi and G. Peter Zhang, "Trend Time–Series Modeling and Forecasting with Neural Networks," IEEE, 2008.
Myatt, G. J. , 2007, Making Sense of Data A Practical Guide to Exploratory Data Analysis and Data Mining. New Jersey: A John Wiley & Sons, inc., publication.
Nagadevara, & Vishnuprasad. , 2005, Building Predictive models for election result in india an application of classification trees and neural network. Journal of Academy of Business and Economics Volume 5 .
Park, T. S., Lee, J. H., & Choi, B. , 2009, Optimization for Artificial Neural Network with Adaptive inertial weight of particle swarm optimization. Cognitive Informatics, IEEE International Conference, 481-485.
Purnomo, M. H., & Kurniawan, A. , 2006, Supervised Neural Network. Suarabaya: Garaha Ilmu.
Rigdon, S. E., Jacobson, S. H., Sewell, E. C., & Rigdon, C. J. , 2009, A Bayesian Prediction Model For the United State Presidential Election. American Politics Research volume.37 , 700-724.
Salappa, A., Doumpos, M., & Zopounidis, C. , 2007, Feature Selection Algorithms in Classification Problems: An Experimental Evaluation. Systems Analysis, Optimization and Data Mining in Biomedicine , 199-212.
Santoso, T. , 2004, Pelanggaran pemilu 2004 dan penanganannya. Jurnal demokrasi dan Ham , 9-29.
Sardini, N. H. , 2011, Restorasi penyelenggaraan pemilu di Indonesia. Yogyakarta: Fajar Media Press.
Shukla, A., Tiwari, R., & Kala, R. ,2010, Real Life Application of Soft Computing. CRC Press.
Sug, H. , 2009, An Empirical Determination of Samples for Decision Trees. AIKED'09 Proceeding of the 8th WSEAS international conference on Artificial intelligence, Knowledge enggineering and data bases , 413-416
Undang-Undang RI No.10 , 2008.
Vercellis, C. , 2009, Business Intelligence : Data Mining and Optimization for Decision Making. John Wiley & Sons, Ltd.
Xiao, & Shao, Q. , 2011, Based on two Swarm Optimized algorithm of neural network to prediction the switch's traffic of coal. ISCCS '11 Proceeding of the 2011 International Symposium on Computer Science and Society , 299-302.
Published
2013-09-15
How to Cite
Badrul, M. (2013). PENERAPAN METODE NEURAL NETWORK UNTUK MEMPREDIKSI HASIL PEMILU LEGISLATIF. Jurnal Techno Nusa Mandiri, 10(2), 161-172. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/514
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Copyright (c) 2013 Mohammad Badrul
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