<![CDATA[Credit is the process of borrowing money from customers to be paid over a certain period of time and with a payment agreement. In general, credit is provided by companies operating in the financial sector such as banks, cooperatives, business credit and finance. In the implementation process, providing credit to customers must be appropriate. In reality, the process of granting credit is still given to the wrong people. The problems faced must be resolved immediately and well, if the problems continue and giving credit not to the right customers will be very detrimental to the company. The settlement process can be done by looking at customer data that has previously received credit. Data mining is a technique that can be used to help solve these problems. In the process of resolving credit granting problems, data mining can be used to process previous credit customer data to obtain a pattern of which customers are eligible for credit. Classification is a method used in data mining to solve various kinds of problems. In this research, research will be carried out using the Naïve Bayes algorithm and the C4.5 algorithm. The method comparison process carried out in the research was carried out to obtain more definite results. This is based on the importance of giving credit to the right person so that there are no problems in the process of completing credit bill payments. Completion of data mining by applying the Naïve Bayes and C4.5 algorithms has been successfully carried out and classification can be carried out for decision making, both algorithms have the same decision making result, namely "Accepted". However, there are differences in the level of accuracy obtained. In the Naïve Bayes algorithm the accuracy level is 86.67%, while in the C4.5 algorithm the accuracy level is 100%.]]>
Copyrights © 2023
