FINANCIAL DISTRESS PREDICTION USING THE DECISION TREE METHOD IN MANUFACTURING COMPANIES IN INDONESIA

Abstract

This study aims to predict financial distress in Indonesian manufacturing companies using the Decision Tree method. A quantitative predictive design was applied to secondary data from the annual financial statements of manufacturing companies listed on the Indonesia Stock Exchange during 2020–2024, yielding 612 firm-year observations. Financial distress was measured as a binary outcome (financially distressed versus non-financially distressed), with Current Ratio, Debt to Asset Ratio, Return on Assets, Total Asset Turnover, Sales Growth, and Firm Size as predictors. Using Python, a pruned Decision Tree achieved 84.6% accuracy, 64.7% precision, 75.9% recall, and a 69.8% F1-score for the financially distressed class. Return on Assets was the most influential predictor, followed by Debt to Asset Ratio and Current Ratio. The resulting rules show that distress reflects interacting conditions of weak profitability, high leverage, low liquidity, inefficient asset utilization, and declining sales growth. Theoretically, the study extends financial distress prediction research by demonstrating the value of interpretable machine learning in an emerging-market manufacturing context. Practically, its transparent rules provide an actionable early-warning tool for investors, creditors, managers, and regulators to identify financial vulnerability and support timely intervention.