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

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 stud...

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Bibliographic Details
Published in:Jurnal Teknologi dan Sistem Komputer
Main Authors: Nurajijah Nurajijah, Dwiza Riana
Format: Article
Language:English
Published: Diponegoro University 2019-04-01
Subjects:
data mining
naive bayes
decision tree
svm
pembiayaan kredit
Online Access:https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13251
Description
Summary: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%.
ISSN:2338-0403

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