<![CDATA[Conducting an evaluation process of prospective debtors is important for creditors to reduce the risk of default. For this reason, the research aims to construct a model that can determine whether a prospective applicant's credit application is recommended to be accepted or rejected by using the method of logistic regression and naïve Bayes classifier. We used a dataset of gender, married, dependent, education, self-employed, applicant income, co-applicant income, loan amount, loan amount term, credit history, and property area as predictor variables and loan status as a response variable. The results show that the performance measures, including accuracy, precision, recall, and F1 score of the logistics regression method, are 85.9%, 83.82%, 100%, and 91.2%, while the naïve Bayes classifier is 84.62%, 83.58%, 98.2%, and 90.32%. Since the performance measures of logistic regression are bigger than naïve Bayes classifier, it suggests that logistic regression is better than naïve Bayes classifier]]>
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