Open AccessOptimization of Decision Tree with PSO on Sharia Cooperative Customer Funding
Author(s) -
Eka Rahmawati,
Candra Agustina
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012022
Subject(s) - c4.5 algorithm , payment , business , loan , sharia , decision tree , particle swarm optimization , remittance , actuarial science , finance , computer science , economics , naive bayes classifier , data mining , artificial intelligence , algorithm , philosophy , theology , support vector machine , islam , economic growth
Credit is a service for Sharia Cooperatives to provide funding to customers who are paid in installments. The accuracy of the customer paying the payments is a determining factor for the smooth operation of the Sharia Cooperative. In addition to timeliness, the ability of customers to pay installments is also the most critical factor for credit returns. Bad credit is a significant threat for Sharia Cooperatives where customers cannot pay payments according to agreed agreements. That makes the prediction of the smooth operation of Sharia Cooperative customers needed. One way that can be done to make predictions is to apply data mining techniques. To make a prediction, several attributes of the customer are used, such as gender, age, status, residency status, number of dependents owned, education, employment, remittance, ceiling, type of loan, credit period, method of payment and collat- eral used. The research will use the J48 as one of the Decision Tree algorithms with particle swarm optimization techniques to improve algorithm performance. The accuracy of J48 with PSO is lower than without PSO.