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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
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PERBANDINGAN ALGORITMA C4.5 DAN NEURAL NETWORK UNTUK MEMPREDIKSI HASIL PEMILU LEGISLATIF DKI JAKARTA
Keywords:
Neural Network Method, C 4.5 Algorithm, Data Mining, Prediksi Hasil Pemilu, Pemilu Legislatif
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. In this study comparison of data mining methods, namely C4.5 and neural network algorithm is applied to both the data legislative candidates to be elected to the legislature and were not selected. C4.5 algorithm is one of the algorithms in a decision tree method that converts the data into a decision tree using the entropy calculation formula. While the neural network algorithm is a method like human neurons to find the best path. From the test results to measure the performance of both methods using cross-validation test method, confusion matrix and ROC curves is known that the neural network has the highest accuracy value which is equal to 98.50%, followed by the C4.5 algorithm method with 97.84% accuracy values. AUC values for the neural network method showed the highest value of 0.982 and a decision tree algorithm with a value of 0.970.
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References
Astuti, E. D. (2009). Pengantar Jaringan Saraf Tiruan. Wonosobo: Star Publishing.
Borisyuk, R., Borisyuk, G., Rallings, C., dan Thrasher, M. (2005). Forecasting the 2005 General Election: A Neural Network Approach. The British Journal of Politics dan International Relations Volume 7, Issue 2, 145-299.
Choi, J. H., dan Han, S. T. (1999). Prediction of Elections Result using Discrimination of NonRespondents: 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.
Gorunescu, F. (2011). Data Mining Concepts, Model and Technique. Berlin: Springer.
Han, J., dan Kamber, M. (2007). Data Mining Concepts and Technique. Morgan Kaufmann publisher
Kothari, C. R. (2004). Research Methodology Methods and Technique. India: New Age International.
Kusrini, dan Luthfi, E. T. (2009). Algoritma Data mining. Yogyakarta: Andi.
Larose, D. T. (2005). Discovering Knowledge in Data. Canada: Wiley Interscience.
Moscato, P., Mathieson, L., Mendes, A., dan 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 dan Sons, inc., publication.
Nagadevara, dan 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 .
Purnomo, M. H., dan Kurniawan, A. (2006). Supervised Neural Network. Suarabaya: Garaha Ilmu.
Rigdon, S. E., Jacobson, S. H., Sewell, E. C., dan Rigdon, C. J. (2009). A Bayesian Prediction Model For the United State Presidential Election. American Politics Research volume.37 , 700724.
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., dan Kala, R. (2010). Real Life Application of Soft Computing. CRC Press.
Undang-Undang RI No.10. (2008). Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. John Wiley dan Sons, Ltd.
Witten, H. I., Eibe, F., dan Hall, A. M. (2011). Data Mining Machine Learning Tools and Techiques. Burlington: Morgan Kaufmann Publisher.
Borisyuk, R., Borisyuk, G., Rallings, C., dan Thrasher, M. (2005). Forecasting the 2005 General Election: A Neural Network Approach. The British Journal of Politics dan International Relations Volume 7, Issue 2, 145-299.
Choi, J. H., dan Han, S. T. (1999). Prediction of Elections Result using Discrimination of NonRespondents: 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.
Gorunescu, F. (2011). Data Mining Concepts, Model and Technique. Berlin: Springer.
Han, J., dan Kamber, M. (2007). Data Mining Concepts and Technique. Morgan Kaufmann publisher
Kothari, C. R. (2004). Research Methodology Methods and Technique. India: New Age International.
Kusrini, dan Luthfi, E. T. (2009). Algoritma Data mining. Yogyakarta: Andi.
Larose, D. T. (2005). Discovering Knowledge in Data. Canada: Wiley Interscience.
Moscato, P., Mathieson, L., Mendes, A., dan 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 dan Sons, inc., publication.
Nagadevara, dan 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 .
Purnomo, M. H., dan Kurniawan, A. (2006). Supervised Neural Network. Suarabaya: Garaha Ilmu.
Rigdon, S. E., Jacobson, S. H., Sewell, E. C., dan Rigdon, C. J. (2009). A Bayesian Prediction Model For the United State Presidential Election. American Politics Research volume.37 , 700724.
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., dan Kala, R. (2010). Real Life Application of Soft Computing. CRC Press.
Undang-Undang RI No.10. (2008). Vercellis, C. (2009). Business Intelligence: Data Mining and Optimization for Decision Making. John Wiley dan Sons, Ltd.
Witten, H. I., Eibe, F., dan Hall, A. M. (2011). Data Mining Machine Learning Tools and Techiques. Burlington: Morgan Kaufmann Publisher.
Published
2014-09-15
How to Cite
Badrul, M. (2014). PERBANDINGAN ALGORITMA C4.5 DAN NEURAL NETWORK UNTUK MEMPREDIKSI HASIL PEMILU LEGISLATIF DKI JAKARTA. Jurnal Pilar Nusa Mandiri, 10(2), 127-138. https://doi.org/10.33480/pilar.v10i2.470
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