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All Journal Multitek Indonesia : Jurnal Ilmiah Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Jihan Martha
Universitas Nusantara PGRI Kediri
Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Transportation

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Application Of The C4.5 Method Based On K-Means Segmentation For Loan Risk Classification At Savings And Loan Cooperative Jihan Martha; Danar Putra Pamungkas; Made Ayu Dusea Widyadara
MULTITEK INDONESIA Vol 20 No 1 (2026): July
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/mtkind.v20i1.13856

Abstract

The Sri Hadi Dharma Savings and Loan Cooperative faces challenges in conducting loan risk analysis, which is still done manually as a result, the decisions made tend to be subjective and inconsistent. This situation has the potential to lead to errors in risk assessment, which could increase the likelihood of non-performing loans. Additionally, the available payment history data is not labeled with risk categories and therefore cannot be directly used in the classification process. This study aims to build a more objective loan risk prediction model by integrating the K-Means and C4.5 algorithms. The data used in this study consists of 7,155 loan payment history records from the 2020-2025 period. The research stages included data preprocessing through cleaning, reduction, creation of a maximum delinquency feature, feature selection, and Min-Max normalization, resulting in 471 unique data points. Next, K-Means was used to form risk groups based on financial characteristics, the clustering results were labeled as low, medium, and high risk categories, then the labeled data were classified using C4.5 with an 80:20 split of training and test data to generate a prediction model. Model evaluation was performed using a confusion matrix with accuracy, precision, recall, and F1-score parameters. The results show that the resulting model is capable of providing predictions with an accuracy rate of 95.79% and generates decision rules that are easy to understand and interpret. Furthermore, the integration of these two methods transforms payment history data into valuable information for identifying members’ risk patterns. This model is effective in supporting more objective, transparent, and measurable decision-making in loan risk management at savings and loan cooperatives
Implementasi K-Means Clustering dalam Pengelompokan Risiko Pinjaman Nasabah pada Koperasi Simpan Pinjam Jihan Martha; Danar Putra Pamungkas; Made Ayu Dusea Widyadara
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 10 No. 3 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gknm9r11

Abstract

Koperasi Simpan Pinjam Sri Hadi Dharma menghadapi tantangan dalam pengelolaan risiko pinjaman karena proses analisis masih dilakukan secara manual dan bergantung pada penilaian subjektif pengelola. Kondisi tersebut dapat memengaruhi objektivitas dalam pengambilan keputusan terkait pengelolaan pinjaman nasabah. Penelitian ini bertujuan mengelompokkan risiko pinjaman nasabah menggunakan algoritma K-Means Clustering berdasarkan karakteristik pinjaman. Data yang digunakan merupakan data riwayat pinjaman nasabah koperasi periode 2021-2025. Tahapan penelitian meliputi preprocessing data, proses clustering menggunakan K-Means yang dievaluasi melalui pengujian stabilitas cluster, serta interpretasi hasil pengelompokan. Hasil penelitian menunjukkan bahwa K-Means mampu membentuk tiga kelompok nasabah dengan karakteristik yang berbeda berdasarkan pola pinjaman dan pembayaran. Pengelompokan yang dihasilkan memberikan informasi yang lebih terstruktur sehingga dapat mendukung analisis risiko pinjaman dan membantu pengambilan keputusan dalam pengelolaan pinjaman nasabah.
Co-Authors Danar Putra Pamungkas, Danar Putra Made Ayu Dusea Widyadara - Universitas Nusantara Kediri, Made Ayu Dusea Widyadara