PREDIKSI KELAYAKAN NASABAH KREDIT MENGGUNAKAN TEKNIK PENGGABUNGAN KLASTERISASI EXPECTATION-MAXIMIZATION DAN KLASIFIKASI NAÏVE BAYES
Customer Data Credit has not been utilized to recognize patterns that can be used to predict New Credit Clients. However, Credit Client data does not have a class label for the classification. Therefore, the Credit Client data is processed first using EM clusteration (expectation-maximization) method to classify the class so that it can be classified using the Naïve Bayes classification. The purpose of this research is to create a data mining model for the prediction of new Credit Customers Feasibility with combined method of EM Classification and Naïve Bayes Classification. From 540 Credit Customers data, there are 142 data of Credit Clients that become cluster 0 or Bad Credit Customer and there are 398 Data of Credit Clients that become cluster 1 or good Credit Customers. The accuracy level of Naïve Bayes classification after using EM clusterization has an average accuracy of 99.2437% and an average error rate of 0.7563%. Thus the combined method of EM clustering and Naïve Bayes classification using WEKA can be concluded to be used to predict the feasibility of New Credit Clients.
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