Analisis Kelayakan Pemberian Kredit dengan Algoritma Naïve Bayes untuk Antisipasi Risiko Kredit Bermasalah Pada BPR Ukabima Lestari Cabang Jambi - Anggi Saputra; Setiawan Assegaff; Benni Purnama (Asosiasi Riset Ilmu Teknik Indonesia, 2025)
This study analyzes creditworthiness assessment and predicts non-performing loan (NPL) risk using the Naïve Bayes algorithm at BPR Ukabima Lestari, Jambi Branch. A quantitative data mining approach with probabilistic classification is applied. The dataset includes borrower attributes such as age, occupation, income, loan amount, tenor, collateral, and repayment history. Research stages comprise data preprocessing, model development, and performance evaluation using accuracy, precision, recall, and F1-score implemented in RapidMiner. The results indicate that the Naïve Bayes model achieves 99.58% accuracy, demonstrating strong capability to predict potential problem loans accurately and efficiently, supporting data-driven credit decisions and strengthening credit risk management in microbanking institutions.