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

Abstract

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.