The banking world in terms of lending to customers is routine activities that are at high risk. In its execution, the problematic credit or bad credit is often due to the lack of careful credit analysis in the process of granting credit, as well as from poor customers. The purpose of this study is to implement data mining to assist in conducting credit analysis process in order to produce the right information whether the customer who will apply for the credit is worthy or not to be able to see the potential payment by the customer. The attributes used in this study consist of 11 attributes i.e. marital status, number of liabilities, age, last education, occupation, monthly income, home ownership, warranties, loan amount, length of loan and description as a result attribute. The methods of data collection used are observation, interviews, and documentation. The method used in this study is K-Nearest Neighbor (K-NN). From the results of evaluation and validation using the K-5 fold that has been done using the RapidMiner tools obtained the highest accuracy results from the K-Nearest Neighbor (K-NN) method of 93.33% in the 5th test.