Article Per Year (5 Year)
p-Index From 2019 - 2024
1.405
P-Index
This Author published in this journals
All Journal Celebes Engineering Journal Nusantara Hasana Journal Jurnal J-PEMAS Jurnal Minfo Polgan (JMP) Journal of Technology Research in Information System and Engineering Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Asrul
Universitas Teknologi Akba Makassar
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Library & Information Science Mathematics Nursing Social Sciences

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Inspiration: Jurnal Teknologi Informasi dan Komunikasi

 1 
Applied of Classification Technique in Data Mining For Credit Scoring Heriyanto Heriyanto; Ika Kurniawati; Fachri Amsury; Muhammad Rizki Fahdia; Irwansyah Saputra; Nanang Ruhyana; Asrul
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 12 No. 2 (2022): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (317.759 KB) | DOI: 10.35585/inspir.v12i2.17

Abstract

In the development of the banking business, credit issues remain interesting to study and uncover. Most of the problems occur not in the system implemented by the bank, but the problem occurs precisely in the human resources who manage credit, either in their relationship with consumers or in errors on the part of the bank which mispredicts in assessing consumers who apply for credit. Several studies in the computer field have been carried out to reduce credit risk which causes losses to the company. In this study, a comparison of the Naive Bayes, C4.5 and KNN algorithms was carried out which was applied to consumer data that received credit eligibility for good and bad customers. The best prediction results are nave Bayes with an accuracy of 95.95% and an AUC of 0.974. The results of this classification are implemented in the form of a website-based application that can be used to facilitate related parties in the credit scoring system.
Co-Authors Adnan Setiawan Ilham Agus Halid Andi Nur Fadillah Andi Yulia Muniar Andi Yulia Muniar Fachri Amsury Fadli Tamrin Febri Hidayat Saputra First Wanita Fitriana M. Sabir Hamdan Gani Heriyanto Heriyanto Ika Kurniawati Irwansyah Saputra Markani Markani Marwa Salehu Mashud, Mashud Muhajirin Muhammad Ichfan Askar Muhammad Qadri Muhammad Rizki Fahdia Muhar Muhajir Nanang Ruhyana Neneng Awaliah Nuraida Latif Ramlah P Restu Ananda Siti Nurhalizah Suryadi Syamsu Tamra Tamra Tamsir Yuyun