Integration of ADASYN Method With Decision Tree Algorithm In Handling Imbalance Class For Loan Status Prediction

Integration of ADASYN Method With Decision Tree Algorithm In Handling Imbalance Class For Loan Status Prediction

• By Rahmawati, Ami; Yulianti, Ita; Mardiana, Tati; Pribadi, Denny;
• Jurnal Nasional Terakreditasi
• 157
• 131-140

Determining the provision of credit is generally carried out based on measuring credibility using credit analysis principles (5C principles). However, this method requires quite a long processing time and is very susceptible to subjective judgments which might influence the final results. This research aims to utilize data mining techniques by developing modeling on loan status prediction datasets. The stages in this research include data preprocessing, modeling and evaluation using accuracy metrics and ROC graphs. In this analysis, it is known that there is a class imbalance in the processed dataset so it is necessary to carry out an oversampling technique. In this research, the ADASYN (Adaptive Synthetic) Oversampling technique is used to ensure the class distribution is more balanced. Then, the ADASYN technique is integrated with the Decision Tree Algorithm to build a prediction model. The research results show that the two methods are able to increase prediction accuracy by 12.22% from 73% to 85.22%. This improvement was obtained by comparing the accuracy results before and after using the ADASYN Oversampling technique. This finding is important because it proves that the implementation of such integration modeling can significantly improve the performance of classification models and can provide strong potential for practical application in helping more effective loan status predictions.

• Katakunci Decision Tree; Imbalance Class; Loans; Oversampling Techniques
• URL https://ejournal.kresnamediapublisher.com/index.php/jri/article/view/299/280
• Penerbit Jurnal Riset Informatika,

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