Comparison Of Optimization Of Algorithm Particle Swarm Optimization And Genetic Algorithm With Neural Network Algorithm For Legislative Election Result

Comparison Of Optimization Of Algorithm Particle Swarm Optimization And Genetic Algorithm With Neural Network Algorithm For Legislative Election Result

• By Badrul, Mohammad; Frieyadie,; Akmaludin,; Ningtyas, Dwi Arum; Sulistyowati, Daning Nur; Nurajiah, Nurajijah;
• Seminar Internasional Bereputasi
• 97
• 78

General Election is one of the characteristics of a democratic country. One of the countries that embrace the democratic system is the state of Indonesia. Elections are a party of democracy in Indonesia to elect representatives of the people who will sit in parliament and provide great opportunities for the people of Indonesia to compete to appoint themselves to become members of the legislature. Research related to the election has been done by researchers is by using decision tree method or by using neural network. each method has its own weaknesses and advantages, but neural network methods can cover the weaknesses of the decision tree. The result of research using neural network method in predicting election result has accurate result value is still less accurate. In this research, we create neural network algorithm model and optimization with particle swarm optimization algorithm to increase attribute weight to all attributes or variables used, select attributes, and feature selection. whereas the Genetic Algorithm for predicting the performance of generalizations based on static properties of networks such as activation function and hidden neurons will be strong enough to find solutions. After testing with neural network algorithm to produce accurate value of 98.50% and AUC value of 0.982, further optimization done with particle swarm optimization obtained an accuracy of 98.85% and AUC value of 0.996. and then done the optimization testing with genetic algorithm obtained an accuracy value of 96.56% and AUC value of 0.925 So that both methods have a difference of accuracy that is equal to 0,35% and difference of AUC value equal to 0,14.

• Katakunci General Election, Neural Network Algorithm, Particle Swarm Optimization, Genetic Algorithm.
• URL http://
• Penerbit ,

REFERENSI

[1]. Aggarwal, C. C. (2015). Data Mining The Textbook. Springer.

[2]. Borisyuk, R., Borisyuk, G., Rallings, C., & Thrasher, M. (2013). Forecasting the 2005 General Election:A Neural Network Approach. The British Journal of Politics & International Relations, 145-299.

[3]. Bradberry, L. A. (2016). The Effect of Religion on Candidate Preference in the 2008 and 2012 Republican Presidential Primaries. Plos One, 1-2.

[4]. Bruce, P. C., Patel, N. R., & Shmueli, G. (2016). Data Mining for Business Analytics : Concepts, Techniques, and Applications with XLMiner 3rd Edition. New York, United States: John Wiley & Sons Inc.

[5]. Brunello, G. H., & Nakano, E. Y. (2015). Bayesian Inference on Proportional Elections. PLOS ONE, 1-2.

[6]. Casey, A. (2016). Soft Computing : Developments, Methods & Applications. New York, United States: Nova Science.

[7]. Demuth, H. B., Hagan, M. T., & Beale, M. H. (2014). Neural Network Design (2nd Edition). Martin Hagan.

[8]. 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.

[9]. Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. San Rafael, CA, United States: Morgan & Claypool Publishers.

[10]. Jones, S. (2015). Genetic Algorithms : Practical Applications. Clanrye International.

[11]. kiranyaz, S., Ince, T., & Gabbouj, M. (2013). Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Springer.

[12]. Lichtendahl, K. C., Bruce, P. C., Patel, N. R., Shmueli, G., Torgo, L., & Yahav, I. (2017). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons Inc.

[13]. 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.

[14]. Mahmud, F., & Zuhori, S. T. (2012). Genetic Algorithm. Germany: LAP Lambert Academic Publishing.

[15]. Mark, E. F., Witten, I., Pal, H. C., & Hall, M. (2017). Data Mining 4th Edition Practical Machine Learning Tools and Techniques. Elshiever Science & Technology.

[16]. Meira, W., & Zaki, M. J. (2015). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge, United Kingdom: Cambridge University Press.

[17]. Moscato, P., Mathieson, L., Mendes, A., & Berreta, R. (2015). 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, (pp. 371-379).

[18]. Nagadevara, & Vishnuprasad. (2013). Building Predictive models for election result in india an application of classification trees and neural network. Journal of Academy of Business and Economics.

[19]. 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.

[20]. Rigdon, S. E., Jacobson, S. H., Sewell, E. C., & Rigdon, C. J. (2013). A Bayesian Prediction Model For the United State Presidential Election. American Politics Research, 700-724.

[21]. Saldana, & Hernandez, H. (2013). Result on Three Predictios for July 2012 Federal Elections in Mexico Based on Past Regularities. Plos One, 1-2.

[22]. Sekretariat Negara, R. (2017). Pemilihan Umum AnggotavDewan Perwakilan Rakyat, Dewan Perwakilan Daerah, DanvDewan Perwakilan Rakyat Daerah. Jakarta: Setneg.

[23]. 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.

[24]. Tan, P. N., Steinbach, M., Karpatne, A., & Kumar, V. (2018). Introduction to Data Mining 2nd Edition. United States: Pearson Education.

[25]. Thoha, M. (2014). Birokrasi Politik Pemilihan Umum di Indonesia. Jakarta: Kencana.

[26]. Walker, B. (2017). Particle Swarm Optimization (PSO) : Advances in Research & Applications. New York, United States: Nova Science Publishers Inc.

[27]. 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.