Synthetic Minority Oversampling Technique pada Averaged One Dependence Estimators untuk Klasifikasi Credit Scoring

  • Omer Heranova STMIK Nusa Mandiri
Keywords: Imbalance class, credit scoring, resampling, Synthetic Minority Oversampling Technique , SpreadSubSample


Bank or financial institution is a business entity whose activities are collecting funds from the public in the form of deposits and channeling them to the public in the form of credit and or other forms. In credit financing problems often occur and one of the problems faced in credit assessment is imbalance class data sets or dataset class imbalances. This problem can be overcome by resampling method, namely by using Oversampling, undersampling and hybrids that combine the two sampling approaches. This research proposes the method of applying SMOTE or Synthetic Minority Oversampling Technique on Averaged One Dependence estimators (AODE) to improve the performance of the accuracy of the credit rating classification on German Credit Creditetsets. The results of this experimental study on the GermanCredit dataset with the classification method without the Resampling process on 13 classifiers produce an average performance value of 70%. The results of the classification with classification techniques that apply the SMOTE method on the AODE algorithm can increase the accuracy performance by 5.5% with an accuracy value of 0.817 or 81.69%. While the classification technique that applies the SpreadSubSample + AODE method decreased by 0.041 or 4.1% but still higher than the accuracy value of other methods with an accuracy value of 0.723 or 72.33%. The researcher concludes that by applying the Resampling technique with the SMOTE method on the AODE algorithm can increase the value of accuracy performance effectively on the imbalance class used for credit scoring or credit rating on GermanCredit datasets.



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Peraturan OJK No.29/POJK.05/2014. Tersedia di [Accessed 22Agust 2019].

He, H, Zhang, W, & Zhang, S.(2018). A novel ensemble method for creditscoring: Adaption of different imbalance ratios. Expert Systems with Applications, 98, 105–117. j.eswa.2018.01.012.

Ethem Alpaydin, (2014). Introduction to Machine Learning, 3rd ed., MIT Press.

Zhang, X, Yang, Y, & Zhou, Z. (2018). A Novel CreditScoring Model based on Optimized Random Forest. Computing and Communicating Workshop and Conference (CCWD), 2018 IEEE 8th Annual, 978(1), 60–65.

Wang, G., Hao, J., Ma, J., & Jiang, H. (2011). A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications, 38(1), 223–230. 06.048.

Ren, F, Cao, P, Li, W, Zhao, D, & Zaiane, O. (2017). Ensemble based adaptive oversampling method for imbalance data learning in computer aided detection of microaneurysm. Computerized Medical Imaging and Graphics, 55, 54–67. 10.1016/j.compmedimag.2016.07.011.

Jian, C, Gao, J, & Ao, Y. (2016). A new sampling method for classifying imbalance data based on supportvector machine ensemble. Neurocomputing, 193, 115–122.

Xiao, J., Xie, L., He, C., & Jiang, X. (2012). Dynamic classifier ensemble model for customer classification with imbalance class distribution. ExpertSystems with Applications,39(3),3668–3675.

Koutanaei, F. N, Sajedi, H, & Khanbabaei, M. (2015). A hybrid data mining model of feature selection algorithms and ensemble learning classifiers for creditscoring. Journal of Retailing and Consumer Services, 27, 11–23.

Han, J, Kamber, M., Pei, J,(2012). Data Minning Concept And Techniques. California: Morgan Kaufmann.

Rajesh, K.N.V.P.S, & Dhuli, R. (2018). Classification of imbalance ECG beats using resampling techniques and AdaBoost ensemble classifier. Biomedical Signal Processing and Control, 41,242-254.

Saifudin, A., Teknik, F., Pamulang, U., Komputer, F. I., Nuswantoro, U. D., & Software, P. C. (2015). Pendekatan Level Data untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software. Journal of Software Engineering, 1(2), 76–85.

Webb, G. I., J. Boughton, and Z. Wang (2005). "Not So Naive Bayes: Aggregating One-Dependence Estimators".

Dawson, C. W. (2009).Projects in Computing and InformationSystems A Student’s Guide (2nd ed.). Pearson Education Limited.

How to Cite
Heranova, O. (2019). Synthetic Minority Oversampling Technique pada Averaged One Dependence Estimators untuk Klasifikasi Credit Scoring. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 3(3), 443 - 450.
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