Bankruptcy Prediction Analysis of Companies In Indonesia Using Ensemble Learning Methods to Improve Prediction Accuracy

Corporate bankruptcy poses significant economic risks, particularly amid post-pandemic uncertainties. Traditional models often fail to capture the nonlinear complexity of financial data. This study improves bankruptcy prediction accuracy for Indonesian companies using ensemble learning techniques, comparing Random Forest, Logistic Regression, and XGBoost. A Systematic Literature Review (SLR) of 23 peer-reviewed studies is combined with financial data from 80 publicly listed Indonesian companies (40 bankrupt, 40 non-bankrupt) covering the period from 2015 to the first quarter of 2025. Models are evaluated using accuracy as the primary metric, with precision, recall, and F1-score as supporting metrics to provide a more comprehensive performance assessment, while feature importance analysis highlights key liquidity and profitability indicators, such as the current ratio, quick ratio, and return on assets (ROA). Results show that XGBoost and Random Forest achieved 93% accuracy, while Logistic Regression achieved 86.7% accuracy, confirming that ensemble models consistently outperform other models, whereas Logistic Regression remains interpretable. Findings provide practical implications for early warning systems, risk assessment, regulatory monitoring, and governance, offering actionable guidance for stakeholders and contributing to the development of data-driven bankruptcy prediction frameworks.