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All Journal Jurnal Aplikasi Manajemen Pendidikan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) Jurnal Ilmiah Akuntansi Manajemen IJISTECH Rengganis Jurnal Pengabdian Masyarakat EKONOMIKA45 Indonesian Community Journal Keizai Jurnal Ekonomi Kreatif dan Manajemen Bisnis Digital (JEKOMBITAL) Profetik Jurnal Ekonomi Syariah
Sigit Mulyanto
Universitas Darwan Ali
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mathematics Public Health Social Sciences Other

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Benchmarking Machine Learning Models for Corporate Bankruptcy Prediction using Financial Ratios Mulyanto, Sigit; Yonia, Dwika Lovitasari; Arif, Muhammad; Sutejo, Bambang
Jurnal Ekonomi Kreatif dan Manajemen Bisnis Digital Vol 4 No 2 (2025): NOVEMBER
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/jekombital.v4i2.1071

Abstract

Corporate bankruptcy prediction is a critical task in financial risk management, particularly under conditions of economic uncertainty and highly imbalanced datasets. This study presents a comprehensive benchmarking framework that evaluates multiple supervised learning models and a voting ensemble approach for corporate bankruptcy prediction. Using a publicly available dataset comprising 78,682 financial records from US-listed companies on NYSE and NASDAQ (1999-2018), we compare the performance of Random Forest, XGBoost, Gradient Boosting, Support Vector Machine, Decision Tree, and a Voting Classifier. Extensive preprocessing, including outlier removal, normalization, and feature selection, and cost-sensitive learning to mitigate severe class imbalance was conducted to ensure data quality. Model performance was assessed using multiple evaluation metrics such as accuracy, F1-score, and ROC AUC to account for class imbalance. Results demonstrate that the Voting Classifier, integrating Random Forest, XGBoost, and Gradient Boosting via hard voting, achieves superior overall performance with an accuracy of 93.6%, F1-score of 96.5%, and ROC AUC of 82.6%, outperforming individual models. The findings underscore the value of ensemble approaches in improving prediction robustness while addressing class imbalance challenges in financial distress forecasting. This study contributes a reproducible experimental design that can guide future research and practical implementation of learning models in corporate bankruptcy risk assessment.
Co-Authors Agung Purwanto Bambang Sutejo Bambang Sutejo, Bambang Dewi Apriliani, Dewi Apriliani Dwi Ardi Wicaksana Putra Dwi Wahyu Prabowo Dwika Lovitasari Yonia Ida Ayu Putu Sri Widnyani Ike Riwayati Ningsih Leni Handayani Lili Winarti Lukman Bachtiar Luvita Anggraini Megawati - Muhammad Arif Nasution, Febriza Damaianti Noor Sentya Wati Nurahman Nurahman, Nurahman Pahlevi, Olvi Salavia Ririn Ririn Rokhman Permadi Sari Hayati Siregar, Rizal Ma'ruf Amidy Siti Maulida, Siti Maulida Situmorang, Kheylina Lidya Wehdawati Yonia, Dwika Lovitasari