<![CDATA[The growth of digital news in Indonesia has seen a significant surge, especially in the economic sector. However, the abundance of information makes it difficult for users to find relevant news. This study aims to classify economic news using the Support Vector Machine (SVM) method and Term Frequency-Inverse Document Frequency (TF-IDF). The process begins with collecting economic news data, preprocessing the text, feature extraction using TF-IDF, and classification using SVM. Evaluation results show that the model achieved a training accuracy of 81.33% , a test accuracy of 76.57% , and an average 5-fold cross-validation accuracy of 77.3%. This indicates high accuracy and stable generalization in classifying news into appropriate categories. This study is expected to assist readers and information systems in efficiently filtering news content.]]>
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