Klasifikasi Kelayakan Pemberian Kredit Pada Calon Debitur Menggunakan Naïve Bayes
 (*)Jasmir Jasmir  (Universitas Dinamika Bangsa, Jambi, Indonesia)
Xaverius Sika (Universitas Dinamika Bangsa, Jambi, Indonesia)
Mulyadi Mulyadi (Universitas Dinamika Bangsa, Jambi, Indonesia)
Rischa Amelia (Universitas Dinamika Bangsa, Jambi, Indonesia)
(*) Corresponding Author |
Abstract
As a lending company, PT. PRIMA KONSUMEN FINANCE certainly has the possibility of bad credit in extending credit to its debtors which can reduce the company's income. Therefore the author performs data mining analysis on debtor data that has borrowed at PT. PRIMA KONSUMEN FINANCE to become valuable information for the company. The author uses debtor data in 2019 as many as 265 data. In conducting the analysis the author uses the WEKA Tools tool. The method used is the Naïve Bayes classification method with 9 attributes. The contribution of this research is to build a creditworthiness classification model for prospective borrowers. This model produces classification performance evaluation values for 3 test options, namely the training set option, 5-fold cross validation and 10-fold cross validation. The contribution of this research is to produce a creditworthiness classification model The results of the Naïve Bayes classification with the greatest percentage of accuracy were obtained using the Training Set, which was 72.8302%, using 5-fold cross validation of 63.3962% and using 10-Fold Cross Validation of 66.4151%.
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