Application of Minning Data for Customer Eligibility Classification in Credit Application Using Naives Bayes and Random Forest Methods

Abstract

The role of data mining is very important in business management that is currently increasingly competitive. Data Mining can use important assets in the company as business data that has a large amount, so that the data can be processed into information. Data mining has several techniques that can be used, one of which is classification. The classification itself has several algorithms, including the Naives Baye algorithm and Random Forest. The advantage of using the Naives Bayes algorithm and random forest is that it uses small training data to determine the estimates on the paremeter needed for a classification process. This research uses the resulting loan parameters, and these parameters can be made an assessment of the loan status at Bank Palembang. So that it can determine the classification of prospective customers who are eligible or not to receive loans. To find out the feasibility of the loan, an accurate forecast with good accuracy is needed. To find out the value of accuracy, you can use technology in the field of Data Mining. The results showed an accuracy value of 78.4% on customer data used in the naives bayes algorithm.