Implementation of Decision Tree Algorithm (J.48) to Predict Risk of Credit in BMT

Abstract

Credit is crucial in financial institutions that affects the growth and development of these institutions. Weak supervision and management in the process of extending credit to customers can lead to high non-performing loans. This problem occured in one of the financial institutions that provides credit to customers, namely BMT X. Data for 2019 showed that there were 600 applications for multipurpose loans. Of these, only about 76% showed good collectability. The condition of credit collectability that is not optimal causes BMT X to spend more to collect installments that must be paid by the debtor directly. This bad credit causes losses to the financial institution. Thus, in providing credit, BMT X must be smart in assessing customer’ feasibility. The purpose of this research is to design credit policies in order to minimize the prediction errors of customers with bad credit category. The technique used in this research is classification data mining with the J.48 algorithm. To measure the effectiveness of an attribute in classifying a data sample set, it is necessary to select the attribute that has the greatest information gain which will be placed at the root node. This research produces six rules with an accuracy level of 80,2% so as it can be used by BMT X to search customer’s feasibility to gain credit.

Keywords: Algorithm J.48, data mining, decision tree, credit risk