PENGELOMPOKAN NASABAH BERDASARKAN TINGKAT RISIKO KREDIT MENGGUNAKAN ALGORITMA K-MEANS

Moh Sutrisno; Denni Pratama

Authors

Moh Sutrisno STMIK IKMI Cirebon, Indonesia
Denni Pratama STMIK IKMI Cirebon, Indonesia

Keywords:

K-Means, risiko kredit, Nasabah, Data Mining, UMKM

Abstract

Perbankan memiliki peran strategis dalam menjaga stabilitas ekonomi melalui penyaluran kredit kepada masyarakat dan pelaku usaha. Namun, proses penilaian risiko kredit yang masih dilakukan secara manual berpotensi menimbulkan bias, keterlambatan pengambilan keputusan, serta meningkatkan risiko kredit macet (Non-Performing Loan/NPL). Oleh karena itu, diperlukan pendekatan berbasis data untuk membantu pengelompokan risiko kredit nasabah secara objektif dan efisien. Penelitian ini menerapkan metode K-Means Clustering untuk mengelompokkan nasabah berdasarkan parameter nilai kredit (plafon), pendapatan, riwayat pembayaran, dan lama hubungan dengan bank. Tahapan penelitian meliputi pengumpulan data, pembersihan data, normalisasi, penerapan algoritma K-Means, serta evaluasi hasil clustering berdasarkan jarak centroid. Sistem dikembangkan menggunakan aplikasi data mining berbasis visual tanpa coding. Hasil penelitian menunjukkan bahwa metode K-Means mampu membentuk beberapa cluster nasabah dengan tingkat risiko berbeda, yaitu risiko rendah, menengah, dan tinggi. Pengelompokan ini dapat membantu pihak bank dalam mengidentifikasi nasabah berisiko tinggi secara lebih akurat serta menjadi dasar dalam penyusunan kebijakan kredit dan strategi mitigasi risiko guna meminimalkan terjadinya kredit macet.

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