Algoritma Naive Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah

Nurajijah Nurajijah (Master of Computer Science, STMIK Nusa Mandiri Jakarta)
Dwiza Riana (Master of Computer Science, STMIK Nusa Mandiri Jakarta)

Jurnal Teknologi dan Sistem Komputer
Volume 7, Issue 2, Year 2019 (April 2019)

The decision on financing approval in sharia cooperatives has a high risk of the inability of customers to pay their credit obligations at maturity or referred to as bad credit. To maintain and minimize risk, an accurate method is needed to determine the financing agreement. The purpose of this study is to classify sharia cooperative loan history data using the Naïve Bayes algorithm, Decision Tree and SVM to predict the credibility of future customers. The results showed the accuracy of Naïve Bayes algorithm 77.29%, Decision Tree 89.02% and the highest Support Vector Machine (SVM) 89.86%.