Ella Nurelasari


Bad credit is one of the credit risk faced by the financial industry and banking. Credit jams can be avoided by means of an accurate credit analysis of borrowers. The accuracy of credit ratings is crucial to the profitability of financial institutions. Improved accuracy of credit ratings can be done by selecting the attributes, because the selection of attributes reduce the dimensionality of the data so that the operation of the data mining algorithms can be run more effectively and more quickly. The purpose of this study is to apply the Particle swarm optimization (PSO) to do the selection of attributes on a Support Vector Machine to improve the accuracy of the accuracy of credit analysis at anugerah.Banyak employee cooperative research has been conducted to determine credit ratings. One of the methods most widely used method of support vector machine. In this study will be used method of support vector machine and will have the attributes using the Particle Swarm Optimization for determining credit ratings. After testing, the results obtained are support vector machine produces accuracy rate 74.74%, 82.03% and AUC values precision value of 0.643. Then the selection of attributes to use, particle swarm optimization in which the attributes which originally numbered 11 predictor variables were selected seven attributes used. The results showed a higher level of accuracy that is equal to 81.36%, 83.08% and AUC values precision value of 0.689. There by achieving an accuracy improvement of 6.62%, and increase the AUC of 0.147. By looking at the values of accuracy and AUC, support vector machine algorithm particle swarm optimization into classification category enough.

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Ahmad Ghodselahi (2011).A Hybrid Support Vector Machine Ensemble Model for Credit Scoring. International Journal of Computer Applications (0975 – 8887)

Aydin, I., Karakose, M., & Akin, E. (2011). A multi-objective artificial immune algorithm for parameter optimization in support vector machine. Journal Applied Soft Computing, 11, 120-129. [3] Abraham, A., Grosan, C., Ramos, V., (2006). Swarm Intelligence in Data Mining. Springer-Verlag Berlin Heidelberg.

Bellotti, T., & Crook, J. (2007) Support vector machines for credit scoring and discovery of significant features. Expert System with Application: An International Journal, 36, 3302-3308.

Dawson, C. W. (2009). Projects in Computing and Information System A Student's Guide. England: Addison-Wesley.

Gorunescu, Florin (2011). Data Mining: Concepts, Models, and Techniques. Verlag Berlin Heidelberg: Springer.

Hian, C.K., Wei, C.T., & Chwee, P.G (2006). A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques. International Journal of Business and Information, 1, 96-118.

Han, J., & Kamber, M. (2006). Data Mining Concepts and technique. San Francisco: Diane Cerra

Jianguo, Z., & Tao, B. (2008). Credit Risk Assessment using Rough Set Theor and GA-based SVM. The 3rd International Conference on Grid and Pervasive Computing, 320-325.

(2004). Keputusan Mentri Negara Koperasi dan Usaha Kecil Menengah Republik Indonesia. Tentang petunjuk pelaksanaan kegiatan usaha koperasi jasa keuangan syariah. No 91

Larose, D. T. (2005).Discovering Knowledge in Data. New Jersey: John Willey & Sons, Inc.

Ming-hui.J., & Xu-chuang, Y. (2007). Construction and Application of PSO-SVM Model for Personal Credit Scoring. ICCS '07 Proceedings of the 7th international conference on Computational Science,158-161.

Mingyuan, Z., Chong, F., Luping, J., Mingtian, Z. (2011). Feature selection and parameter optimization for support vector machines: A new approach based on genetic algorithm with feature chromosomes. Expert Systems with Applications: An International Journal, 38, 5197-5204.

Maimon, O., & Rokach, L. (2010). Data Mining and Knowledge Discovery Handbook (2nd ed). New York: Springer Dordrecht Heidelberg London

Ping, Y. (2009). Feature selection based on SVM for credit scoring. International. Conference on Computational Intelligence and Natural Computing, 2, 44-47.

Rivai, V., & Veithzal, A.P. (2006). Credit Management Handbook. Jakarta: Raja GrafindoPersada.

Pressman, Roger.S. (2001)."Software Engineering : A Practioner's Approach." 5th McGrawHill.

Santosa, B. (2007). Data Mining: Teknik Pemanfaatan Data untuk Keperluan Bisnis. Yogyakarta: Graha Ilmu.

Satsiou, A.,Doumpos, M., & Zopounidis, C. (2007) Genetic Algorithms for the Optimization of Support Vector Machines in Credit Risk Rating. Technical University of Crete Dept. of Production Engineering and Management Financial Engineering Laboratory.

Shuzhou, W., & Bo, M. (2011). Parameter Selection Algorithm for Support Vector Machine. Procedia Environmental Sciences, 11, 538-544.

UndangUndang Perbankan No.10 Tahun 1998.

UndangUndang Usaha Kecil No.9 Tahun 1995.

Vercellis, Carlo (2009). Business Intelligent: Data Mining and Optimization for Decision Making. Southern Gate, Chichester, West Sussex: John Willey & Sons, Ltd.

Witten, I. H., Frank, E., & Hall, M. A. (2011).Data Mining: Practical Machine Learning and Tools. Burlington: Morgan Kaufmann Publisher.

Wei, X., Shenghu, Z., Dongmei, D. & Yanhui, C.(2010). A Support Vector Machine Based Method For Credit Risk Assessment. IEEE 7th International Conference on e-Business Engineering, 50-55.

Yun, L., Qiu-yan, C. & Hua, Z. (2011). Application of the PSO-SVM model for Credit Scoring. Seventh International Conference on Computational Intelligence and Security, 47-51.



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