KOMPARASI ALGORITMA KLASIFIKASI DATA MINING DALAM PENENTUAN RESIKO KREDIT PADA KOPERASI SERBA USAHA - Nandang Iriadi (Paradigma, 2013)
Growth and development of the cooperative lately , where lending credit cooperatives thatcater to its members to develop business in the workshop , stores , services and so on . No doubt ,lend funds to member cooperatives will surely emerge problems , such as members of theborrower paying the overdue installment of funds , misuse of funds for other purposes , thecustomer fails to develop its business so as to result in cooperative funds do not flow or it can leadto bad credit . In this study, using the method of comparison between the three models , namelyNaive Bayes , Neural Network and k - nearest neighbor , the cooperative member data todetermine which is the most accurate method for determining credit risk in business cooperativesthat will produce cooperative members who pay installments smoothly or delinquent in thepayment of the loan installments . From the results of the study to measure the performance of thethree algorithms using the test method validation . Matrikx confusion , note that the k - nearestneighbor method has the highest accuracy value of 93.00 % , for the Naive Bayes method has anaccuracy value of 90.33 % , while the method which has the lowest value is the Neural Networkmethod has an accuracy value of 85.67 % . As for the value of Area Under the Curve ( AUC ) forthe k - nearest neighbor method has the highest score is 0989 , Naive Bayes method is 0.619 ,while the lowest value method with the method of Neural Network 0.467 .Keywords : comparison , naive Bayes , neural network , k - nearest neighbor .