Abstract

The development of regional banking provides an opportunity for the public to save and borrow to improve the standard of living and business capital. Analysis of potential customer predictions is carried out to reduce credit risk. The risk of crediting is a credit jam or the bank no longer receives interest and installments regularly. This research was conducted to reduce the risk of credit defaults in Lampung regional banks. This research uses the C45 algorithm as a method that helps determine alternative customers who can receive credit from the Lampung area. The C45 algorithm method is used as a decision tree classification model to help determine alternative customers who can receive credit from the Lampung district. To form a decision tree, attributes needed are used as a reference for classification, in this study six attributes are used, namely Address, Age, gender, marital status, employment, income and electricity bills.