Pendekatan Hybrid Clustering – Classification Untuk Klasifikasi Risiko Kredit Menggunakan K-Means Dan Random Forest
DOI:
https://doi.org/10.29407/5h88rh95Abstract
Risiko kredit merupakan salah satu tantangan utama dalam lembaga keuangan karena dapat menyebabkan kerugian finansial dan mengganggu stabilitas operasional. Penelitian ini bertujuan mengklasifikasikan risiko kredit menggunakan pendekatan hybrid yang mengombinasikan K-Means dan Random Forest. Dataset yang digunakan terdiri dari 7.155 data pinjaman dengan atribut besar pinjaman, sisa pinjaman, angsuran, tenor, dan tunggakan maksimum. K-Means digunakan untuk membentuk label risiko secara otomatis berdasarkan pola data, yang selanjutnya dimanfaatkan sebagai target klasifikasi dan fitur tambahan pada Random Forest. Hasil penelitian menunjukkan bahwa model hybrid memiliki performa lebih baik dibandingkan model baseline dengan akurasi 100%, sedangkan model baseline mencapai 98,94%. Analisis feature importance menunjukkan bahwa fitur cluster dan tunggakan maksimum merupakan faktor yang paling berpengaruh dalam menentukan risiko kredit. Temuan ini menunjukkan bahwa pendekatan hybrid efektif meningkatkan kinerja klasifikasi pada dataset tanpa label.
Keywords:
Klasifikasi, Clustering, Risiko Kredit, K-Means, Random Forest##plugins.themes.default.displayStats.downloads##
References
[1] A. A. Montevechi, R. de C. Miranda, A. L. Medeiros, and J. A. B. Montevechi, "Advancing credit risk modelling with Machine Learning: A comprehensive review of the state-of-the-art," Eng. Appl. Artif. Intell., vol. 137, p. 109082, Nov. 2024, doi: 10.1016/J.ENGAPPAI.2024.109082.
[2] A. Primajaya and B. N. Sari, "Random Forest Algorithm for Prediction of Precipitation," Indonesian Journal of Artificial Intelligence and Data Mining, vol. 1, no. 1, pp. 27-31, Mar. 2018, doi: 10.24014/IJAIDM.V111.4903.
[3] D. Ismafillah et al., "Analisis algoritma pohon keputusan untuk memprediksi penyakit diabetes menggunakan oversampling smote," INFOTECH: Jurnal Informatika & Teknologi, vol. 4, no. 1, pp. 27-36, Jun. 2023, doi: 10.37373/INFOTECH.V4I1.452.
[4] K. P. Sinaga and M. S. Yang, "Unsupervised K-means clustering algorithm," IEEE Access, vol. 8, pp. 80716-80727, 2020, doi: 10.1109/ACCESS.2020.2988796.
[5] F. P. F. Pangestu, N. Y. N. Yasin, R. C. R. C. Hasugian, and yunita yunita, "Penerapan Algoritma K-Means Untuk Mengklasifikasi Data Obat," Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 12, no. 1, pp. 53-62, Mar. 2023, doi: 10.32736/SISFOKOM.V1211.1461.
[6] F. D. Rahman, M. I. Z. Mulki, and A. Taryana, "CLUSTERING DAN KLASIFIKASI DATA CUACA CILACAP DENGAN MENGGUNAKAN METODE K-MEANS DAN RANDOM FOREST," Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi, vol. 1. no. 2, pp. 90-97, Apr. 2024, doi: 10.61124/SINTA.V112.15.
[7] W. Novrian, A. Afriani, and J. Purnama Sari, "Segmentation of Problematic Loan Customers Using the K-Means Clustering Algorithm to Support Strategic Decision-Making (Case Study: Bank Mega Finance Bengkulu)", doi: 10.70656/ijcse.v2i01.499.
[8] S. Nur Illah, N. Suarna, I. Ali, and D. Solihudin, "Journal of Artificial Intelligence and Engineering Applications K-Means Clustering Method to Make Credit Payment Groupinhg Efficient," 2025. [Online]. Available: https://ioinformatic.org/
[9] M. Ardiansyah and Supatman, "Klasterisasi Tagihan Pada Nasabah Pinjaman Online Menggunakan Metode K-Means Clustering," Jurnal RESTIKOM: Riset Teknik Informatika dan Komputer, vol. 6, no. 2, pp. 286-294, Aug. 2024, doi: 10.52005/RESTIKOM.V6I2.318.
[10] F. Damayanti, A. Budiman, S. Sundari, and T. M. Nainggolan, "Classification of Customer Credit Risk Levels Using the Random Forest Method: A Case Study on Microfinance Institutions," Journal of Computer Science Artificial Intelligence and Communications, vol. 1, no. 2, pp. 52-56, Nov. 2024, doi: 10.64803/JOCSAIC.V112.20.
[11] N. Listiana Hanun and A. Udin Zailani, "PENERAPAN ALGORITMA KLASIFIKASI RANDOM FOREST UNTUK PENENTUAN KELAYAKAN PEMBERIAN KREDIT DI KOPERASI MITRA SEJAHTERA", doi: 10.37365/jti.v6i1.61.
[12] M. Rizal Ubaidillah, N. Adhi Santoso, E. Ungguk Sedya Utami, and S. YMI Tegal, "Penerapan K-Means Clustering untuk Klasifikasi Jenis Beras Berdasarkan Tren Harga di Pasar Induk Beras Cipinang (PIBC)," RIGGS: Journal of Artificial Intelligence and Digital Business, vol. 4, no. 3, pp. 4300-4306, Sep. 2025, doi: 10.31004/riggs.v4i3.2594.
[13] A. Kuraria, N. Jharbade, and M. Soni, "Centroid Selection Process Using WCSS and Elbow Method for K-Mean Clustering Algorithm in Data Mining," Int. J. Sci. Res. Sci. Eng. Technol., pp. 190-195, Dec. 2018, doi: 10.32628/ijsrset21841122.
[14] N. K. Zuhal, D. P. Pamungkas, and R. Wulaningrum, "Klasifikasi Emosi Pada Wajah Dengan Menggunakan K-MEANS Clustering dan KDEF," Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), vol. 5, no. 1, pp. 243-248, Aug. 2021, doi: 10.29407/INOTEK.V511.955.
[15] T. Y. Wen and S. A. Mohd Aris, "Hybrid Approach of EEG Stress Level Classification Using K-Means Clustering and Support Vector Machine," IEEE Access, vol. 10, pp. 18370-18379, 2022, doi: 10.1109/ACCESS.2022.3148380.
[16] R. D. Nugraha, D. D. Adelia, and D. Rivaldi, "Segmentasi Pelanggan Retail Berbasis Perilaku menggunakan Algoritma K-Means Clustering," Digital Transformation Technology, vol. 5, no. 2, pp. 141-148, Oct. 2025, doi: 10.47709/DIGITECH.V512.6340.
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