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All Journal Jurnal Ilmu Komputer Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Jurnal Ilmiah Mahasiswa FEB Suhuf Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JRST (Jurnal Riset Sains dan Teknologi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI MATAPPA: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Kebidanan Jurnal ULTIMATICS JSiI (Jurnal Sistem Informasi) JURNAL PENELITIAN KESEHATAN Jurnal Sistem Informasi dan Informatika (SIMIKA) Al-Kharaj: Journal of Islamic Economic and Business Multica Science and Technology Decode: Jurnal Pendidikan Teknologi Informasi Aptekmas : Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Satya Informatika
Arief Wibowo
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Analysis Of Online Loan Regional Clustering in Indonesia In 2024 Based On Outstanding And Default Rate (TWP90) Using K-Means Clustering Fina Sherli Wewengkang; Arief Wibowo
Al-Kharaj: Journal of Islamic Economic and Business Vol. 8 No. 1 (2026): All articles in this issue include authors from 3 countries of origin (Indonesi
Publisher : LP2M IAIN Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24256/kharaj.v8i1.9342

Abstract

The increase in online lending distribution in Indonesia in 2024 was not accompanied by a uniform level of credit risk across regions. This study aims to categorize online lending regions in Indonesia based on outstanding values ​​and 90-day default rates (TWP90) using a quantitative approach based on the K-Means algorithm. Secondary data from all provinces was analyzed using RapidMiner and evaluated using the Davies–Bouldin Index (DBI). The test results showed a DBI of 0.746 at K=2, 0.376 at K=3, and 0.564 at K=4. Although K=2 yielded the lowest DBI, the K=3 model was chosen because it provided a more informative and policy-relevant risk classification. The clustering resulted in three risk clusters: Low Risk, with outstanding values ​​and TWP90 below average; Medium Risk, with values ​​above average; and High Risk, characterized by a very high TWP90 level despite relatively low outstanding values. These findings confirm the effectiveness of K-Means in mapping online lending risks based on regions and support more precise credit monitoring. Keywords: online loans, K-Means clustering, default risk, outstanding, TWP90.
Co-Authors Aal Hibsy Adang Rochiyat Afrizal " Antonius Yansen Suryadarma5 Assaye Girma Mengistu Dinana Izzatul Ulya Dwi Hastuti, Yussi Fazar Sidik Fina Sherli Wewengkang Iwan Irawan Januar Malik Mahardika Mas'ud Hermansyah Mirwansyah, Dedy Muhammad Fawwaz Nani Retnowati Nuraeny Septianti NURHAYATI . Rachmah Indawati Raden Anggia Apriani Djumhana Reza Rahman Rifky Aditia Hamdan Riri Fajriah Silvia Tri Margianing Mukti Surahmat, Asep Surma Elisa Manihuruk Tamara Nur Budiarti Tiaharyadini, Rizka Zaqi Kurniawan