Implementation of Machine Learning Algorithm for Credit Scoring Prediction in Islamic Microfinance - Siregar, Kiki Hardiansyah; Ruslan, Dede; Faried, Annisa Ilmi; Sembiring, Rahmad (Journal of Intelligent Systems and Information Technology, 2025)
Islamic microfinance institutions face complex challenges in data management and customer behavior prediction in the digital era. This study aims to optimize the Gradient Boosting algorithm with pruning techniques to predict customer collectibility. The analysis was conducted on data from 57 customers with 7 attributes from 2022 to 2024. The research methodology includes four stages: data collection, pre-processing, modeling, and evaluation. Pre-processing involves handling missing data, normalization, encoding, and feature selection. Modeling using XGBoost with and without pruning, followed by evaluation using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve metrics. The results show an increase in model performance with pruning: accuracy increased by 0.70%, precision 0.60%, recall 0.80%, and F1-score 0.70%. This technique is effective in reducing overfitting and increasing model generalization. This research provides significant contribution in developing more accurate credit scoring system for Islamic microfinance institutions, improving credit risk management and customer service in Islamic microfinance sector. The findings help Islamic microfinance institutions optimize credit decision-making process and reduce risk in the digital era.