PENERAPAN PARTICLE SWARM OPTIMAZATION UNTUK MENEN-TUKAN KREDIT KEPEMILIKAN RUMAH DENGAN MENGGUNAKAN ALGORITMA C4.5

  • Mulkan Syarif (1*) Manajemen Informatika AMIK BSI Jakarta

  • (*) Corresponding Author
Keywords: Data Mining, C4.5 Algorithm, Particle Swarm Optimazation

Abstract

In studies that have been done previously to determine ownership loan home. One of the methods of the most widely used method with a high degree of accuracy is the C4.5 algorithm. In conducting this study also used a method algorithm C4.5 and to improve the accuracy will be performed using the addition of particle swarm optimization method for the determination of credit ratings. Homeownership after testing the results obtained is a support vector machine produces a value of 91.93% accuracy and AUC value of 0.860 was then performed using particle swarm optimization method in which the attributes which originally totaled 8 predictor variables selected from eight attributes used. The results showed higher accuracy value that is equal to 94.15% and AUC value of 0.941. So as to achieve an increased accuracy of 2.22% and an increase in AUC of 0.081. By looking at the accuracy and AUC values, the algorithm of support vector machines based on particle swarm optimization and therefore is in the category of classification is very good.

 

References

Abraham, A., Grosan, C., Ramos, V., (2006). Swarm Intelligence in Data Mining. Springer-Verlag Berlin Heidelberg.

Gang, W., Jinxing, H., Jian, M., &Hongbing, J. (2011).A comparative assessment of ensemble learning for credit scoring.Expert Systems with Applications: An International Journal. 38, 223-230.

Han, J., &Kamber, M. (2006). Data Mining Concept and Tehniques. San Fransisco: Morgan Kauffman.

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

Olson, D, & Shi, Y. (2008).PengantarIlmuPenggalian Data Bisnis. Jakarta: PenerbitSalembaEmpat.

Sausa & etc, (2004), Particle swarm based Data Mining Algorithms for classification tasks. ACM Digital Library.

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

Yi Jiang, Yan Chen, ZhimingZeng, &Xiangjian He.(2009). A Bank Customer Credit Evaluation Based on the Decision Tree and the Simulated Annealing Algorithm. World Congress on Computer Science and Information Engineering,18-22.

Zhang, &, etc., (2010. Vertical bagging decision trees model for credit scoring. Elsevier Ltd.

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.
Published
2015-09-15
How to Cite
Syarif, M. (2015). PENERAPAN PARTICLE SWARM OPTIMAZATION UNTUK MENEN-TUKAN KREDIT KEPEMILIKAN RUMAH DENGAN MENGGUNAKAN ALGORITMA C4.5. Jurnal Techno Nusa Mandiri, 12(2), 147-156. https://doi.org/10.33480/techno.v12i2.445
Article Metrics

Abstract viewed = 38 times
PDF downloaded = 64 times