Penentuan Penilaian Kredit Menggunakan Metode Naive Bayes Berbasis Particle Swarm Optimization

Rinawati Rinawati


Bad credit is one of the credit risk faced by the financial and banking industry. Bad credit can be avoided by means of an accurate credit analysis of the debtor. The accuracy of credit ratings is crucial to the profitability of financial institutions. Improved accuracy of credit ratings can be done by doing the selection of attributes, because the selection of attributes reduce the dimensionality of the data so that operation of the data mining algorithms can be run more effectively and more cepat.Banyak research has been conducted to determine credit ratings. One of the methods most widely used method of Naive Bayes. In this study will be used method Naive Bayes and will do the selection of attributes by using particle swarm optimization to determine credit ratings. After testing the results obtained are Naive Bayes produce accuracy value of 72.40% and AUC value of 0.765. Then be optimized by using particle swarm optimization results show values higher accuracy is equal to 75.90% and AUC value of 0.773. So as to achieve the increased accuracy of 3.5%, and increased the AUC of 0.008. By looking at the accuracy and AUC values, the Naive Bayes algorithm based on particle swarm optimization into the classification category enough.

Full Text:



B. Indonesia, Juni 2012. [Online]. Available: DE7C023507CE/26392/BISPIApril20121.pdf.

T. Bellotti and J. Crook, "Support Vector Machines For Credit Scoring And Discovery Of Significant Features," Expert System With Application, vol. 36, no. 2, pp. 3302-3308, 2009.

G. Wang, J. Hao, J. Ma and H. Jiang, "A Comparative Assessment Of Ensemble Learning For Credit Scoring," Expert System With Applications: An International Journal, vol. 38, no. 1, pp. 223-230, 2011.

P. Yao, "Feature Selection Based On SVM For Credit Scoring," International Conference on Computational Intelligence and Natural Computing, pp. 44-47, 2009.

H. C. Koh, W. C. Tan and C. P. Goh, "A Two-step Method To Construct Credit Scoring Models With Data Mining Technigues," International Journal of Business and Information, vol. 1, pp. 96-118, 2006.

Kusrini and E. T. Luthfi, Algoritma Data Mining, Yogyakarta: ANDI, 2009.

S. Wang and B. Meng, "Parameter Selection Algorithm for Support Vector Machine," Procedia Environmental Sciences, vol. 11, pp. 538-544, 2011.

L. Yun, Q.-y. Cao and H. Zhang, "Application of the PSO-SVM model for Credit Scoring," Seventh International Conference on Computation Intelligence and Security, pp. 47-51, 2011.

A. Heiat, "Modeling Consumer Credit Scoring Through Bayes Network," World Journal of Social Sciences, vol. 3, pp. 132-141, 2011.

O. Maimon and L. Rokach, Data Mining and Knowledge Discovery Handbook, London: Springer , 2010.

C. Grosan, A. Abraham and M. Chis, "Swarm Intelligence in Data Mining," Studies in Computational Intelligence (SCI), vol. 34, pp. 1-20, 2006.

D. Aha and F. G. S. a. U. Irvine, "UCI Machine Learning Repository,", 2007. [Online]. Available:



  • There are currently no refbacks.

J-SAKTI (Jurnal Sains Komputer & Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan :

View My Stats