Implementation of Mechine Learning Eligibility for Customer Credit Payments at Bank BTN Using the K – Nearst Neighbor Algorithm

Abstract

Credit is the provision of money or bills that can be equated with that, based on a loan agreement or agreement between a bank and another party that requires the borrower to pay off the debt after a certain period of time with interest (Government of Indonesia, 1998). In its initial development, credit had a function in stimulating mutual assistance aimed at meeting needs, both in the field of business and meeting daily needs.In developing applications, it is necessary to predict applications at Bank BTN Medan accurately, accurate prediction results are very important in showing the right rating and decision-making in selecting customers. When customers experience arrears, the system used by Bank BTN Medan is still manual and has not applied predication in credit arrears to customers of Bank BTN Medan. Tests carried out in this test use a credit customer dataset from Bank BTN Medan. This study predicts the eligibility of customer credit payments at Bank BTN with the K – Nearst neighbor algorithm. The prediction of the level of smoothness of credit payments is made using K-Nearest Neighbor in order to be able to predict the smoothness of future credit payments.