<![CDATA[The COVID-19 pandemic has intensified financial distress across various industries in Indonesia, especially in sectors like Accommodation and food & beverage, Other services, and Transportation & Warehousing. This situation highlights the urgent need for an accurate early warning system that can predict financial distress using reliable classification algorithms for business continuity. This research compares the Performance of the Support Vector Machine (SVM) and Decision Tree classification algorithms in predicting financial distress. The study utilizes secondary data from annual financial reports of companies listed on the Indonesia Stock Exchange (IDX) from 2019 to 2023. The research focuses on the Accommodation and food & beverage, Other services, and Transportation & Warehousing sectors. Data is collected using a purposive sampling method, ensuring balance across observations. A quantitative data analysis approach with an experimental design is applied to evaluate the classification performance. The results indicate that the Decision Tree algorithm outperforms SVM in all key Performance metrics: accuracy, precision, recall, and F1-score. The Decision Tree achieves perfect classification results, while SVM exhibits lower predictive ability, particularly in recall and F1-score. These findings suggest that the Decision Tree is more effective for financial distress prediction in this dataset. The study contributes to financial risk assessment by demonstrating the practical application of machine learning in financial classification tasks. Future research can enhance this model by incorporating larger datasets and developing application-based implementations to improve decision-making processes in corporate financial management.]]>
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