Determination Priority of Credit Assessment Aspect by Using a Data Mining

Abstract

Abstract - Loan application can be assessed with a variety of assessment approaches . The Approach that is often used is the 5C aspect, ie Character , Capital, Capacity, Collateral and Conditions of Economic. Every aspect in the assessment 5C have the same important priority to each other. Character aspect often expressed as aspects that have the highest priority in the initial assessment of a loan application. Credit applicant who has Character/ good temperament is needed to provide security for the lender/ bank in the payment of existing obligations. Currently, Debtor Information System ( SID ) or known as BI Checking has been used by creditors to assist in the analysis at the beginning of the loan application, further, the applicant credit will be surveyed and analyzed based on 5C aspects credit application. Weka is a collection of machine learning algorithms for data mining tasks. It used in this study to determine which aspects of a priority in the assessment of an application for approval of credit. The research results on the credit application data before utilizing SID / BI checking, obtained with the order of priority aspects of assessment, namely capital, capacity, collateral, character and condition of economic. Character position is at the fourth (4) of the five (5) aspects of assessment. Once the SID/BI Checking is used, a change in the order of priority aspects of the assessment collectibility, capacity, condition of economic, capital, collateral and character.