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

Naïve Bayes, Decision Tree, and SVM Algorithm for Classification of Sharia Cooperative Customer Financing Approval

Master of Computer Science, STMIK Nusa Mandiri Jakarta, Indonesia

Received: 16 Jan 2019; Revised: 26 Apr 2019; Accepted: 29 Apr 2019; Available online: 16 Jul 2019; Published: 30 Apr 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
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%.
Keywords: data mining; Naive Bayes; Decision Tree; SVM; credit financing
Funding: STMIK Nusa Mandiri Jakarta

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