Article Per Year (5 Year)
p-Index From 2021 - 2026
1.235
P-Index
This Author published in this journals
All Journal Jurnal Ilmu Lingkungan Teknika Jurnal Rekayasa Sistem Industri Jurnal Online Mahasiswa (JOM) Bidang Ilmu Ekonomi SITEKIN: Jurnal Sains, Teknologi dan Industri Industria: Jurnal Teknologi dan Manajemen Agroindustri Journal of Industrial Engineering Management Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri Masyarakat Madani: Jurnal Kajian Islam dan Pengembangan Masyarakat Jurnal Economica: Media Komunikasi ISEI Riau
Lestari, Fitra
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Implementation of Data Mining to Classify Potential Customers Using the C5.0 Algorithm Rizki, Muhammad; Maghfirah, Cintya Nil; Norhiza, Fitra Lestari; Nofirza, Nofirza; Lubis, Fitriani Surayya
JTI: Jurnal Teknik Industri Vol 11, No 1 (2025): JUNI 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v11i1.39167

Abstract

PT Pegadaian, as listed on its official website, is a fast-growing financial company. One of its key challenges is late installment payments, which can lead to financial losses. Using pawn customer data from 2013 to 2021, this study found that out of 534 customers, 68 were late in paying installments, and 10 did not pay. To address this issue, this research applies customer classification to identify borrowers who are more likely to pay on time. The classification model is developed using data mining with the C5.0 algorithm to generate decision-tree rules. Prior to modeling, the dataset is processed through the Knowledge Discovery in Databases (KDD) stages, including data selection, cleaning, and transformation. The proposed model produces 26 classification rules and achieves an accuracy of 87.04%. All data processing, modeling, and validation are conducted using RapidMiner Studio. Keywords: Classification, Decision Tree, C5.0 Algorithm, Data Mining
Co-Authors ANDRINI, ROZI anwardi, anwardi Aulia Tama, Dewanda Budi Azwar Devi, Safitri HAMDALAH, AHMAD Harpito, Harpito Irham Hamzah Zah Juandi Juandi Julina Julina Lubis, Fitriani Surayya Maghfirah, Cintya Nil Maureka, Raka Misra Hartati Muhammad Afdal muhammad iqbal rachman Muhammad Rizki Mupida, Siti Nofirza Nofirza Novisa, Anthon Randa Surya Dinata Ria Nelly Sari Rika Taslim Syafmelia, Firjatullah Syaputra, Widi Tengku Nurainun Titi Antin Umam, Muhammad Isnaini Hadiyul Umam, Muhammad Isnaini Umam Yuslenita Muda Zukrianto, Zukrianto