Penerapan Data Mining Dalam Pemberian Kelayakan Kredit Nasabah Pada Badan Usaha Milik Desa Gedong Gincu Dengan Metode Naïve Bayes

BUMDes Gedong Gincu is a rural business that provides savings and loan services to village communities. BUMDes Gedong Gincu in determining customers is still very simple, namely by looking at where the customer comes from and how close they are to previous customers who have applied for credit, so it is not a guarantee whether or not the new customer is eligible to get a loan at BUMDes Gedong Gincu. Another problem that occurs is the lack of thoroughness by BUMDes administrators in assessing customers. The aim of this research is to apply the Naïve Bayes method in determining the credit worthiness of customer applications. The method used in this research is Naïve Bayes with testing using a confusion matrix. Research stages include data selection, data cleaning, data transformation, application of the Naïve Bayes method, testing. The result of this research is an application that can facilitate BUMDes Gedong Gincu in evaluating the feasibility of providing credit. The test results using 186 data using the confusion matrix method, an accuracy of 67% was obtained. However, based on SUS testing by users, they got a result of 82.25. This indicates that this application is good and suitable for use by BUMDes Gedong Gincu.