<![CDATA[This study aims to develop an early warning system to detect potential financial distress at PT Garudafood Putra Putri Jaya Tbk using Machine Learning (ML) algorithms. A quantitative case study approach was applied using the company’s publicly available quarterly financial reports from 2019 to 2024. Five ML algorithms were tested: Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The Random Forest algorithm achieved the highest accuracy of 85.71%, with a precision of 100%, recall of 66.67%, and an F1-score of 80%. Operating Profit Margin and Debt to Asset Ratio were identified as the most significant indicators in detecting financial distress. The proposed model can serve as a decision-support tool for management, investors, and regulators to proactively anticipate financial problems and reduce bankruptcy risk through early intervention. Keywords: Abstract, aim, scope, result]]>
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