Pengaruh Prediksi Kredit bermasalah Terhadap Keputusan Bank Dengan Komparasi Metode ANN, CNN, dan Random Forest
DOI:
https://doi.org/10.52436/1.jpti.1276Keywords:
ANN, CNN, Kebijakan, kredit bermasalah, random forestAbstract
Kredit bermasalah menjadi salah satu tantangan utama sektor perbankan karena dapat mengganggu stabilitas keuangan dan mempengaruhi keputusan strategis. Oleh karena itu, kemampuan memprediksi potensi kredit bermasalah secara akurat sangat penting dalam pengelolaan risiko. Penelitian ini membandingkan kinerja tiga algoritma kecerdasan buatan ANN (Artificial Neural Network) dengan arsitektur MLP (Multilayer Perceptron), CNN (Convolutional Neural Network), dan RF (Random Forest) untuk klasifikasi risiko kredit berdasarkan data nominatif kredit.Dataset mencakup fitur-fitur utama seperti plafon pinjaman, jangka waktu, penghasilan, baki debet, kolektibilitas dan total tunggakan. Data diklasifikasikan dalam lima kategori risiko berdasarkan status pembayaran antara lain L (Lancar), DP (Dalam Perhatian), KL (Kurang Lancar), D (Diragukan), dan M (Macet). Setiap model dievaluasi berdasarkan akurasi keseluruhan dan kemampuannya mengidentifikasi kelas minoritas dan mayoritas.Hasil menunjukkan bahwa Random Forest unggul dengan akurasi 91,42%, efektif dalam mengklasifikasikan kategori “Lancar” dan “Macet”. Model ANN MLP mencapai akurasi 89,90%, namun kurang optimal untuk kelas minoritas. Sementara CNN hanya mencatat akurasi 58,58% dan mengalami overfitting terhadap kelas mayoritas.Studi ini menyimpulkan bahwa Random Forest adalah metode paling tepat untuk data tabular yang seimbang, sementara CNN memiliki potensi dalam konteks data spasial atau sekuensial. Dan hasil yang akurat dari prediksi Random forest diharapkan dapat menjadi pertimbangan bagi lembaga keuangan dalam pengambilan keputusan terkait kebijakan di bank Pemerintah di Purbalingga.
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