PENERAPAN MODEL CRISP-DM PADA PREDIKSI NASABAH KREDIT MENGGUNAKAN ALGORITMA RANDOM FOREST

Dwi Bagus Saputra; Vihi Atina; Faulinda Eli Nastiti

doi:10.36080/idealis.v7i2.3244

Dwi Bagus Saputra Fakultas Ilmu Komputer, Program Studi Teknik Informatika, Universitas Duta Bangsa, Surakarta, Indonesia
Vihi Atina Fakultas Ilmu Komputer, Program Studi Teknik Informatika, Universitas Duta Bangsa, Surakarta, Indonesia
Faulinda Eli Nastiti Fakultas Ilmu Komputer, Program Studi Teknik Informatika, Universitas Duta Bangsa, Surakarta, Indonesia

DOI: https://doi.org/10.36080/idealis.v7i2.3244

Keywords: Savings and Loan Cooperative, Non-Performing Loans, Machine Learning, Credit Prediction, Random Forest

Abstract

Non-performing loans (NPLs) are one of the main challenges faced by Baitut Tamwil Tazakka Savings and Loan Cooperative, which can potentially threaten the financial stability and health of the institution. This study aims to evaluate the effectiveness of the Random Forest algorithm in predicting NPLs in the cooperative. The CRISP-DM method is applied in this study, encompassing the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The data used consists of 14 attributes and 190 records that have been cleaned of missing values. The modeling results show that the Random Forest algorithm can provide very high prediction accuracy, with the best accuracy reaching 94.8% on a 90:10 dataset split. Performance metrics evaluation such as AUC, CA, F1 Score, Precision, Recall, and MCC indicate very good values, signifying strong predictive performance. Confusion matrix analysis also confirms high prediction accuracy with most correct predictions in the categories of non-performing, performing, and sub-performing loans. This study confirms that the Random Forest algorithm is effective in predicting NPLs, underscores the importance of applying machine learning in credit risk management, and contributes significantly to the financial stability of the cooperative through more accurate NPL predictions.

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