Optimasi Neural Network Dengan Algoritma Genetika Untuk Prediksi Hasil Pemilukada
Abstract
Abstrak : Indonesia merupakan salah satu negara demokratis di dunia ini. Negara Indonesia yang terdiri dari beberapa kepulauan melahirkan berbagai macam suku dan budaya. Negara Indonesia yang terdiri dari beberapa kepulauan dibagi menjadi 33 provinsi. Negara Indonesia merupakan Negara demokratis. Pemilu yang diselenggarakan di Indonesia adalah untuk memilih pimpinan baik Presiden dan wakil presiden, anggota DPR, DPRD, dan DPD. Penelitian yang berhubungan dengan pemilu sudah pernah dilakukan oleh peneliti yaitu dengan menggunakan metode decision tree atau dengan menggunakan neural network Dalam penelitian ini dibuatkan model algoritma neural network dan model algoritma neural network berbasis algoritma genetika. Setelah dilakukan pengujian dengan dua model yaitu algoritma neural network dan algoritma genetika maka hasil yang didapat adalah algoritma neural network menghasilkan nilai akurasi sebesar 98,50 % dan nilai AUC sebesar 0,982, namun setelah dilakukan penambahan yaitu algoritma neural network berbasis algoritma genetika nilai akurasi sebesar 93.03 % dan nilai AUC sebesar 0,971.Kata kunci : algoritma genetika, akurasi, pemilu, neural networkAbstract : Indonesia is one of the democratic countries in the world. State of Indonesia which consists of several islands spawned various tribes and cultures. State of Indonesia which consists of several islands divided into 33 provinces. Indonesia is a democratic country. Elections were held in Indonesia is to choose the heads of both the president and vice president, members of Parliament, Parliament and Council. Research relating to the election had been conducted by researchers is using decision tree method or by using a neural network In this study created a model neural network algorithms and neural network algorithm model based on genetic algorithms. After testing the two models of neural network algorithms and genetic algorithms then the results obtained is a neural network algorithm produces a value of 98.50% accuracy and AUC value of 0.982, but after the addition of a neural network algorithm that is based on a genetic algorithm accuracy value of 93.03 % and AUC value of 0.971.Keyword: accuracy, elections, genetic algoritm, neural network algorithmReferences
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Berndtssom M, Hansson J, Olsson B, Lundell B. 2008. A Guide for Students in Computer Science and Information Systems. London: Springer.
Bidgoli BM, Punch WF. 2003. Using Genetic Algorithms for Data Mining Optimization in an Educational Web-based System. Proceeding GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII , 2252-2263.
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 JH, & Han ST. 1999. Prediction of Election Result using Descrimination of Non-Respondents: The Case of the 1997 Korea Presidential Election.
Dawson CW. 2009. Projects in Computing and Information System A Student's Guide. England: Addison-Wesley.
Gill GS. 2005. Election Result Forecasting Using two layer Perceptron Network. Journal of Theoretical and Applied Information Technology Volume 4 no.11 , 144-146.
Gorunescu F. 2011. Data Mining Concept Model Technique. India: Springer.
Gray DE. 2004. Doing Research in the Real World. New Delhi: SAGE.
Han J, Kamber M. 2007. Data Mining Concepts and Technique. Morgan Kaufmann publisher.
Haupt RL, Haupt SE. 2004. Practical Genetic Algorithm. Canada: A John Wiley & Sons.
Ileana L, Rotar C, Incze A. 2004. The Optimization of Feed Forward Neural Networks Structure Using Genetic Algorithm. Proceeding IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1 , 762-767 .
K GS, Deepa DS. 2011. Analysis of Computing Algorithm using Momentum in Neural Networks. Journal of computing, volume 3, issue 6 , 163-166.
Ke J, Liu X. 2008. Empirical Analysis of Optimal Hidden Neurons in Neural Network Modeling for Stock Prediction. 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application volume 02 , 828-832 .
Kothari CR. 2004. Research Methology methodes and Technique. India: New Age Interntional.
Kusrini, Luthfi ET. 2009. Algoritma Data mining. Yogyakarta: Andi.
Kusumadewi S, Purnomo H. 2005. Penyelesaian Masalah optimasi dengan teknik-teknik HEURISTIK. Yogyakarta: Graha Ilmu.
Larose DT. 2006. Data Mining Methods and Models. Hoboken, New Jersey: John Wiley & Sons, Inc.
Larose DT. 2005. Discovering Knowledge in Data. Canada: Wiley Interscience.
Moscato P, Mathieson L, Mendes A, Berreta R. 2005. The Electronic Primaries:Prediction The U.S. Presidency Using Feature Selection with safe Data. ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38 , 371-379 .
Nagadevara, Vishnuprasad. 2005. Building predictive models for election results in India an application of classification trees and neural networks. Journal of Academy of Business and Economics volume 5 .
Purnomo MH, Kurniawan A. 2006. Supervised Neural Network. Suarabaya: Garaha Ilmu.
Rigdon SE, Jacobson SH, Sewell EC, Rigdon CJ. 2009. A Bayesian Prediction Model for the United State Presidential Election. American Politics Research July 2009 vol. 37 no. 4 , 700-724.
Santoso T. 2004. Pelanggaran Pemilu dan penanganannya. Jakarta: The Habibie Center.
Sardini NH. 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 Proceedings 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. Southern Gate, Chichester, West Sussex: 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 Proceedings of the 2011 International Symposium on Computer Science and Society , 299-302.
Published
2016-06-01
How to Cite
BADRUL, Mohammad.
Optimasi Neural Network Dengan Algoritma Genetika Untuk Prediksi Hasil Pemilukada.
BINA INSANI ICT JOURNAL, [S.l.], v. 3, n. 1, p. 229 - 242, june 2016.
ISSN 2527-9777.
Available at: <https://460290.0x60nl4us.asia/index.php/BIICT/article/view/820>. Date accessed: 01 dec. 2024.
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Articles
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