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Prediksi Timing Financial Distress Pada Bank Perkreditan Rakyat di Indonesia Menggunakan Machine Learning

Maysas Yafi' Urrochman(1*),Endang Setyati(2),Yosi Kristian(3)
(1) Institut Sains dan Teknologi Terpadu Surabaya
(2) Institut Sains dan Teknologi Terpadu Surabaya
(3) Institut Sains dan Teknologi Terpadu Surabaya
(*) Corresponding Author
DOI : 10.35889/jutisi.v12i2.1219

Abstract

There is no system that can provide early warning of financial problems that threaten the operations of Rural Banks (BPR), so it is necessary to predict the timing of financial distress in BPRs in Indonesia using a two-stage classification and regression technique. Researchers used BPR financial report data in Indonesia for 4 years as a research sample, with a total of 150 Financial Ratio Data, consisting of 50 bankrupt financial ratio data and 100 non-bankrupt BPRs. Data analysis was carried out 2 years before being declared bankrupt. The target classification is divided into 5 classes: very healthy, healthy, moderately healthy, less healthy, distressed. The results of the study concluded: a two-stage classification and regression technique can be used to predict the timing of financial distress. This is evidenced by the results of the MLP Classifier classification with an accuracy rate of f1-score of 87%. The results of the evaluation of timing predictions using Random Forest Regression showed a mean absolute error of 1.8 months and a mean absolute percentage error of 4%.

Keywords: Rural Banks; Financial Distress; Random Forest Regression; Support Vector Machine

Abstrak

Belum ada suatu sistem yang dapat memberikan peringatan dini adanya permasalahan keuangan yang mengancam operasional Bank Perkreditan Rakyat (BPR), sehingga perlu memprediksi timing financial distress pada BPR di Indonesia menggunakan teknik dua tahap klasifikasi dan regresi. Peneliti menggunakan data laporan keuangan BPR di Indonesia selama 4 tahun sebagai sampel penelitian, dengan jumlah data 150 Data Rasio Keuangan, terdiri dari 50 Data rasio keuangan Pailit dan 100 BPR tidak pailit. Analisis Data dilakukan 2 tahun sebelum dinyatakan Pailit. Target klasifikasi dibagi menjadi 5 kelas: sangat sehat, sehat, cukup sehat, kurang sehat, distress. Hasil penelitian menyimpulkan: teknik dua tahap klasifikasi dan regresi dapat digunakan untuk memprediksi timing financial distress. Ini dibuktikan dengan hasil klasifikasi MLP Classifier dengan tingkat akurasi f1-score sebesar 87%. Hasil evaluasi prediksi timing menggunakan Random Forest Regression menunjukkan hasil mean absolute error sebesar 1,8 bulan dan hasil mean absolute percentage error sebesar 4%.

 

Keywords


Bank Perkreditan Rakyat; Financial Distress; Random Forest Regression; Support Vector Machine

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