Indonesian Journal of Artificial Intelligence and Data Mining
Vol 2, No 1 (2019): March 2019

Data Mining Optimization Using Sample Bootstrapping and Particle Swarm Optimization in the Credit Approval Classification

Andre Alvi Agustian (Pamulang University)
Achmad Bisri (Pamulang University)



Article Info

Publish Date
31 Mar 2019

Abstract

Credit approval is a process carried out by the bank or credit provider company. Where the process is carried out based on credit requests and credit proposals from the borrower. Credit approval is often difficult for banks or credit providers. Where the number of requests and classifications must be made on various data submitted. This study aims to enable banks or credit card issuing companies to carry out credit approval processes effectively and accurately in determining the status of the submissions that have been made. This research uses data mining techniques. This study uses a Credit Approval dataset from UCI Machine Learning, where there is a class imbalance in the dataset. 14 attributes are used as system inputs. This study uses the C4.5 and Naive Bayes algorithms where optimization is needed using Sample Bootstrapping and Particle Swarm Optimization (PSO) in the algorithm so that the results of the research produce good accuracy and are included in the good classification. After using the optimization, it produces an accuracy rate of C4.5 which is initially 85.99% and the AUC value of 0.904 becomes 94.44% with the AUC value of 0.969 and Naive Bayes which initially has an accuracy value of 83.09% with an AUC value of 0.916 to 90 , 10% with an AUC value of 0.944.

Copyrights © 2019






Journal Info

Abbrev

IJAIDM

Publisher

Subject

Computer Science & IT

Description

Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM) is an electronic periodical publication published by Puzzle Research Data Technology (Predatech) Faculty of Science and Technology UIN Sultan Syarif Kasim Riau, Indonesia. IJAIDM provides online media to publish scientific ...