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All Journal Proceeding of the Electrical Engineering Computer Science and Informatics
Wardhani, Nia Kusuma
Universitas Mercu Buana
Computer Science & IT Electrical & Electronics Engineering

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The Feasibility of Credit Using C4.5 Algorithm Based on Particle Swarm Optimization Prediction Siswanto, Siswanto; Abdussomad, Abdussomad; Gata, Windu; Wardhani, Nia Kusuma; Gata, Grace; Prasetyo, Basuki Hari
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.2019

Abstract

Credit is a belief that one is given to a person or other entity which is concerned in the future will fulfill all the obligations previously agreed. The objective of research is necessary to do credit analysis to determine the feasibility of a credit crunch, through credit analysis results, it can be seen whether the customer is feasible or not. The methods are is used to predict credit worthiness is by using two models, models classification algorithm C4.5 and C4.5 classification algorithm model based Particle Swarm Optimization (PSO). After testing with these two models found that the result C4.5 classification algorithm generates a value of 90.99% accuracy and AUC value of 0.911 to the level diagnostics Classification Excellent, but after the optimization with C4.5 classification algorithm based on Particle Swarm Optimization accuracy values amounted to 91.18% and the AUC value of 0.913 to the level of diagnosis Excellent Classification. These both methods have different accuracy level of 0.18%.
Co-Authors Abdussomad, Abdussomad Gata, Windu Grace Gata, Grace Prasetyo, Basuki Hari Siswanto, Siswanto