PREDIKSI HASIL PEMILU LEGISLATIF DKI JAKARTA DENGAN METODE NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION
Keywords:
Neural Network Algorithm, Particle Swarm Optimization Algorithm, Election
Abstract
General elections are a means of implementation of the sovereignty of the people in the unitary state of Indonesia based on Pancasila and 1945 Constitution. Elections held in Indonesia is to choose the leadership of both the president and vice president, member of parliament, parliament, and the DPD. The related research of general election usually using decision tree algorithm neural network algorithm. Each of the methods has strengths and weakness, but a neural network algorithm can solve a problem in the decision tree algorithm. The accuracy using a neural network algorithm in predicting the election has less accurate. In this study created a model neural network algorithm and neural network algorithm model based genetic algorithm to get the rule in predicting the outcome of legislative elections and provide a more accurate value of accuracy. After testing the two models namely neural network algorithm and neural network algorithm based on particle swarm optimization, the results obtained are the neural network algorithm produces an accuracy value by 98,50% and the AUC value of 0.982, but after the addition of neural network algorithm based on particle swarm optimization value of 98,85 % accuracy and AUC value of 0.996. So both methods have an accuracy rate of 0.35 % difference and the difference in the AUC of 0.14.
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Borisyuk, R., Borisyuk, G., Rallings, C., & Thrasher, M. , 2005, Forecasting the 2005 General Election:A Neural Network Approach. The British Journal of Politics & International Relations Volume 7, Issue 2 , 145-299.
Choi, J. H., & Han, S. T. , 1999, Prediction of Elections Result using Descrimination of Non-Respondents:The Case of the 1997 Korea Presidential Election.
Dawson, C. W. , 2009, Projects in Computing and Information System A Student's Guide. England: Addison-Wesley.
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 Methology methodes and Technique. India: New Age Interntional.
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.
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 engineering 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
2013-03-15
How to Cite
Badrul, M. (2013). PREDIKSI HASIL PEMILU LEGISLATIF DKI JAKARTA DENGAN METODE NEURAL NETWORK BERBASIS PARTICLE SWARM OPTIMIZATION. Jurnal Techno Nusa Mandiri, 10(1), 37-47. Retrieved from https://ejournal.nusamandiri.ac.id/index.php/techno/article/view/558
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