<![CDATA[The Kemala Aman Finance Bengkulu Cooperative also provides loan facilities for consumers, but not all loan applications will be approved. So far, the determination of the feasibility of applying for a loan is seen from several aspects including marital status, number of dependents, age, last education, occupation, monthly income, home ownership, collateral, number of loan applications, length of loan application. These aspects are analyzed manually by the survey team by filling out the form provided, and then the survey results are given to superiors to be followed up on whether the loan application is accepted or rejected. The loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative is used to make it easier to determine the eligibility of prospective borrowers to be given loans based on marital status, number of dependents, age, last education, occupation, income, home ownership, collateral, number of loan applications, length of loan application. This application is made using the Visual Basic .Net programming language which can be accessed by the Kemala Aman Finance Bengkulu Cooperative Admin. Comparative analysis of the K-Nearest Neighbor method and the Naive Bayes method was carried out by looking at the level of accuracy by comparing the classification results of the two methods with the real data from the classification results obtained from the Kemala Aman Finance Cooperative Bengkulu. Based on the processing time, the KNN method is faster than the Naive Bayes method. Based on the level of accuracy, the KNN method has the highest level of accuracy compared to the Naive Bayes method. Based on the tests that have been carried out, the functionality of the loan eligibility application at the Kemala Aman Finance Bengkulu Cooperative runs as expected, and the application is able to display the results of the classification of loan eligibility through the KNN Method and the Naive Bayes Method.]]>
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