K-Means Clustering Method to Make Credit Payment Groupinhg Efficient

Siti Nur Illah; Nana Suarna; Irfan Ali; Dodi Solihudin

doi:10.59934/jaiea.v4i2.815

Authors

Siti Nur Illah STMIK IKMI Cirebon
Nana Suarna
Irfan Ali STMIK IKMI Cirebon
Dodi Solihudin STMIK IKMI Cirebon

DOI:

https://doi.org/10.59934/jaiea.v4i2.815

Keywords:

K-Means Clustering, Credit Grouping, Data Mining, Credit Risk

Abstract

Credit payment management is one of the main challenges in the financial sector, especially in grouping customers based on risk and payment patterns. This study aims to evaluate the K-Means Clustering method in improving the efficiency of credit payment data clustering. The dataset used includes information on payment history, loan amount, tenor, and credit status from financial institutions. The research approach involves data processing stages, application of the K-Means algorithm, and evaluation of results using the Davies-Bouldin Index and Silhouette Score metrics. The results of the analysis show that the K-Means method is effective in identifying customer payment patterns and dividing them into three main clusters: high, medium, and low risk. In addition, this study found that determining the optimal number of clusters using the Elbow Method can improve the accuracy of the clustering results. The resulting model makes a significant contribution to credit risk management, helping financial institutions make strategic decisions related to credit policies and risk mitigation. This study offers practical implications, including increased operational efficiency and predictive ability against potential bad debts. Further studies are recommended to integrate this method with other algorithms to improve the performance of large-scale data analysis.

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References

N. Ahsina, F. Fatimah, and F. Rachmawati, “Analisis Segmentasi Pelanggan Bank Berdasarkan Pengambilan Kredit Dengan Menggunakan Metode K-Means Clustering,” J. Ilm. Teknol. Infomasi Terap., vol. 8, no. 3, 2022, doi: 10.33197/jitter.vol8.iss3.2022.883.

M. R. Fahlevi, D. Ridha, D. Putri, and E. Syahrin, “Analisis Pengelompokan Data Pelelangan Barang Dengan Metode K-Means Clustering,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., vol. 8, no. 1, pp. 53–61, 2023.

Holwati, E. Widodo, and W. Hadikristanto, “Pengelompokan Untuk Penjualan Obat Dengan Menggunakan Algoritma K-Means,” Bull. Inf. Technol., vol. 4, no. 3, pp. 408–413, 2023, doi: 10.47065/bit.v4i3.848.

R. Limia Budiarti and G. Cendana, “Klasifikasi Data Nasabah Kredit Pinjaman Menggunakan Data Mining Dengan Metode K-Means Pada Mega Central Finance,” J. Akad., vol. 14, no. 2, pp. 88–94, 2022, doi: 10.53564/akademika.v14i2.866.

A. Saputra and R. Yusuf, “Perbandingan Algoritma DBSCAN dan K-MEANS dalam Segmentasi Pelanggan Pengguna Transportasi Publik Transjakarta Menggunakan Metode RFM,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 4, pp. 1346–1361, 2024, doi: 10.57152/malcom.v4i4.1516.

A. C. Putra and K. D. Hartomo, “Optimalisasi Penyaluran Bantuan Pemerintah Untuk UMKM Menggunakan Metode Fuzzy C-Means,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, pp. 474–482, 2021.

N. Astuti, J. N. Utamajaya, and A. Pratama, “Penerapan Data Mining Pada Penjualan Produk Digital Konter Leppangeng Cell Menggunakan Metode K-Means Clustering,” JURIKOM (Jurnal Ris. Komputer), vol. 9, no. 3, p. 754, 2022, doi: 10.30865/jurikom.v9i3.4351.

S. Dwididanti and D. A. Anggoro, “Analisis Perbandingan Algoritma Bisecting K-Means dan Fuzzy C-Means pada Data Pengguna Kartu Kredit,” Emit. J. Tek. Elektro, vol. 22, no. 2, pp. 110–117, 2022, doi: 10.23917/emitor.v22i2.15677.

M. N. R. Fitriani, B. Priyatna, B. Huda, A. L. Hananto, and T. Tukino, “Implementasi Metode K-Means Untuk Memprediksi Status Kredit Macet,” J. Sist. Komput. dan Inform., vol. 4, no. 3, p. 554, 2023, doi: 10.30865/json.v4i3.5953.

Ondra Eka Putra and Randy Permana, “Hybrid Data Mining For Member Determination And Financing Prediction In Syariah Financing Saving And Loan Cooperatives,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 8, no. 2, pp. 309–320, 2024, doi: 10.29207/resti.v8i2.5683.

B. Jimbaran, “OPTIMIZATION OF K-MEANS CLUSTERING USING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR GROUPING TRAVELER REVIEWS DATA ON TRIPADVISOR SITES a I Made Satria Bimantara, b I Made Widiartha,” vol. 12, no. 1, pp. 1–10, 2023.