<![CDATA[problem that often occurs is the difficulty in determining the average bad credit spread across 7,823 savings and loan cooperatives in Indonesia. The main problem faced by savings and loan cooperatives is the difficulty in identifying and mitigating credit risks that can cause bad credit. Bad credit not only harms cooperatives, but can also disrupt the financial stability of cooperative members. The lack of effective tools to measure and predict credit risk makes cooperatives potentially face unnecessary losses. The aim of this research is to apply the C4.5, SVM, and KNN algorithms in determining the average non-performing loans of savings and loan cooperatives, comparing the results and performance of the three such algorithms in the context of credit risk management, and improve understanding of the use of machine learning techniques in identifying credit risk patterns that may be difficult to detect manually. The application of the C4.5 Algorithm, SVM (Support Vector Machine), and KNN (K-Nearest Neighbors) models in determining the average bad credit in the context of savings and credit cooperatives is carried out by considering the appropriate configuration. This research first collects and preprocesses data which includes credit history, income, length of membership, and other related factors from savings and loan cooperatives. Next, factor analysis and feature selection are carried out to identify the factors that most influence credit risk. The results of the three models are evaluated using various evaluation metrics, such as accuracy, precision, recall, F1-score, and AUC-ROC. The results of this research The results show that the SVM model has the highest performance in predicting credit risk, followed by the C4.5 and KNN algorithms. Careful feature selection and robust model validation are also key components in accurate credit risk assessment. Thus, the results of this research can help cooperatives better manage credit risk and make more informed decisions regarding loan approvals.]]>
