Abstract
This study aims to predict the smoothness of installment payments in cooperatives, making it easier for staff to analyze credit lending. Lack of prudence in analyzing credit results in customers who are in arrears in paying installments, resulting in bad credit. To minimize errors that exist, it is necessary to evaluate the provision of loans to prospective debtors. By utilizing past member criteria data in the past that will be used to predict smooth payments using data mining. The data mining technique used is the Naive Bayes classifier method. The prediction process uses the naive Bayes method, namely by determining the probability or opportunity based on the previous member's data, and the results are used to help make a decision. The criteria used are member data: employment, income, house status, number of credits, and type of credit. Based on the naive Bayes method, the results obtained are 90.00% accuracy, 0.880% AUC, 83,33% recall, and 100% precision.
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