Open AccessAlgoritma Naïve Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah
Author(s) -
Nurajijah Nurajijah,
Dwiza Riana
Publication year - 2019
Publication title -
jurnal teknologi dan sistem komputer
Language(s) - English
Resource type - Journals
eISSN - 2620-4002
pISSN - 2338-0403
DOI - 10.14710/jtsiskom.7.2.2019.77-82
Subject(s) - naive bayes classifier , decision tree , support vector machine , credibility , loan , maturity (psychological) , bayes' theorem , business , computer science , actuarial science , data mining , artificial intelligence , finance , political science , bayesian probability , law
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%.