Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
OPTIMASI ALGORITMA NEURAL NETWORK DENGAN ALGORITMA GENETIKA DAN PARTICLE SWARM OPTIMIZATION UNTUK MEMPREDIKSI HASIL PEMILUKADA
Indonesia has one of the islands spread from Sabang to Merauke. State of Indonesia which consists of several islands gave birth to a
wide variety of ethnic and cultural diversity. State of Indonesia which consists of several islands divided into 34 provinces. Indonesia is one country
that adheres to the democratic system in the world. to achieve this goal, one of which is seen at the democratic party to choose the future leaders
who will represent the people in parliament. 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. The method used was limited without doing optimization method for the algorithm. In this study, researchers will conduct research focusing on the optimization using genetic algorithm optimization and particle swarm optimization with the aid of neural network algorithms. After testing the two models of neural network algorithms and genetic algorithms are the results obtained by the neural network algorithm ptimization particle swarm optimization algoritmasi accuracy value amounted to 98.85% and the AUC value of 0.996. While the neural network algorithm with genetic algorithm optimization accuracy values of 93.03% and AUC value of 0.971
Astuti, E. D. (2009). Pengantar Jaringan Saraf Tiruan. Wonosobo: Star Publishing.
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.
Gorunescu, F. (2011). Data Mining Concepts, Model and Technique. Berlin: Springer.
Han, J., & Kamber, M. (2007). Data Mining Concepts and Technique. Morgan Kaufmann publisher
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.
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.
Undang-Undang RI No.10. (2008).
Vercellis, C. (2009). Business Intelligence : Data Mining and Optimization for Decision Making. John Wiley & Sons, Ltd.
Abstract viewed = 283 times
PDF downloaded = 290 times
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
An author who publishes in the Pilar Nusa Mandiri: Journal of Computing and Information System agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-NonCommercial 4.0 License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal
- Author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgement of its initial publication in this journal.
- Author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work (See The Effect of Open Access).
Read more about the Creative Commons Attribution-NonCommercial 4.0 Licence here: https://creativecommons.org/licenses/by-nc/4.0/.