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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi dan Manajemen Informatika CommIT (Communication & Information Technology) Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Antivirus : Jurnal Ilmiah Teknik Informatika Journal of Information System,Graphics, Hospitality and Technology Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknologi Informasi dan Terapan (J-TIT) Journal of Robotics and Control (JRC) Jurnal Teknika Teknika Insyst : Journal of Intelligent System and Computation JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer International Journal of Robotics and Control Systems J-Intech (Journal of Information and Technology) Joutica : Journal of Informatic Unisla Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Inovasi Teknologi dan Edukasi Teknik
Yosi Kristian
Institut Sains Dan Teknologi Terpadu Surabaya
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Journal : Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi

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Prediksi Timing Financial Distress Pada Bank Perkreditan Rakyat di Indonesia Menggunakan Machine Learning Maysas Yafi' Urrochman; Endang Setyati; Yosi Kristian
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 2: Agustus 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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 MachineAbstrakBelum 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%. 
Co-Authors Adriel Ferdianto Ajeng Restu Kusumastuti Aji Prasetya Wibawa Aji Wibawa Albertus Josef Andika Andy Hermawan Anggay Luri Pramana Anik Nur Handayani Arya Tandy Hermawan Ayyub Bayu Anggara Putra Chastine Fatichah Citra Mahaputri Damayanti, Elok Darlis Herumurti Devi Dwi Purwanto Dhaniswara, Erwin Edwin Pramana Effendy Hadi Sutanto Eha Renwi Astuti Eka Rahayu Setyaningsih Elkana Lewi Santoso Endang Setyati Endang Setyati Endang Setyati Esa Prakasa Esther Irawati Setiawan Evan Kusuma Susanto Fitra Hidayah Gunawan Gunawan Gunawan Gunawan Gunawan Hariadi Yutanto Hartono, Nickolas Hatem Alsadeg Ali Salim Hendrawan Armanto Hendrawan, William Hartanto Hermawan Andika Heru Wahyu Herwanto I Ketut Eddy Purnama Joan Santoso Kenny Vincent Kevin Setiono Kms Yusuf Effendi Kurniawan S, Putu Widiarsa Lukman Zaman Luthfi Alwi M. Najamudin Ridha Masrur Anwar Mauridhi Hery Purnomo Maysas Yafi' Urrochman Muhammad Harris Syafa'at Nur Nafi'iyah Nur Nafi’iyah Putra, Ramadhan Hardani Retno Wardhani Riko Gunawan Sandhopi Shandy Darmawan Stephen Ekaputra Limantoro Suriani Alamgunawan Surya Sumpeno Wahyu Sakti Gunawan Irianto Wahyu Widodo Wikjatmiko, Zhulfi Bajra William Sugiarto YOGA DWI WAHYU NUGRAHA Yuliana Melita Pranoto Yulianto, Masrizal Eka Yunan Kholilul Fatah Zaeni, Ilham Ari Elbaith Zuhan, Miftahuz