The Implementasi Paralel K-Means Clustering dan Decision Tree untuk Deteksi Risiko Kredit Macet
DOI:
https://doi.org/10.37859/jf.v16i2.12095
Abstract
Non-Performing Loan (NPL) pose a major threat to the stability of financial institutions. This study implements parallel K-Means Clustering and Decision Tree methods in RapidMiner 2026.0.5 to identify potential credit default risks using the Credit Risk Dataset. The dataset consists of 32,576 borrower records with 12 attributes after filtering. Preprocessing includes handling missing values, dummy coding, converting loan_status into a binary variable, and applying Z-Transformation normalization. The Multiply operator enables K-Means (k=3) and Decision Tree to run simultaneously. The Clustering results show that Cluster 0 (33.10%) is a low-risk group dominated by homeowners with mortgages and an A loan rating; Cluster 1 (32.07%) is a medium-risk group dominated by tenant borrowers with a loan rating of B, and Cluster 2 (34.83%) is a high-risk group dominated by a history of delinquency, loan ratings of C–G, and the highest interest rates. The Decision Tree model achieved an Accuracy of 89.26%, Precision of 76.30%, Recall of 73.05%, and an F-Measure of 74.58%, making it effective as an early detection system for nonperforming loans.
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