<![CDATA[The 2026 ASEAN Championship (AFF Cup) is a prestigious football tournament in Southeast Asia that consistently captures public interest, particularly regarding the identification of potential top scorers. Historically, identifying these candidates has relied on subjective observation rather than data-driven analysis. This study aims to apply the Support Vector Machine (SVM) algorithm to classify potential top scorers based on performance statistics—specifically goals, shots, and assists—recorded over the past year. Secondary data was utilized, comprising five potential players from each of the ten participating nations, resulting in a dataset of 50 players. The research process involved data collection, preprocessing, Min-Max normalization, class label assignment, splitting the data into training and testing sets (80:20 ratio), applying the SVM algorithm, and evaluating the model using a confusion matrix, accuracy, precision, recall, and F1-score. The results indicate that the SVM algorithm successfully classified 17 players into Class A (High Potential to be Top Scorer), 17 players into Class B (Potential to be Top Scorer), and 16 players into Class C (Low Potential to be Top Scorer). Model evaluation yielded an accuracy of 60.00%, demonstrating the SVM algorithm's ability to objectively classify potential top scorers based on performance statistics. This study is expected to serve as a reference for developing machine learning-based football performance analysis and to support the identification of potential top scorers prior to the tournament.]]>
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