<![CDATA[Swimmer performance assessment in Indonesia still largely depends on coaches’ intuition, which may lead to subjective decisions and inconsistencies in training program planning, particularly in environments where frequent changes in coaches and sports administrators occur. The lack of structured and data-driven performance assessment tools further limits the continuity and objectivity of athlete development. This study aims to develop a web-based system capable of predicting swimmers’ performance potential by estimating race times based on physical characteristics using the XGBoost model. The proposed system is designed to support coaches in identifying athlete performance potential in a more objective and data-driven manner. Model evaluation results indicate that the XGBoost model achieved an R² value of 0.9190, demonstrating a very high level of prediction accuracy, with an average prediction time of 7.036 seconds. Software testing results confirm that the system operates as intended and is able to present prediction outputs in the form of estimated swimming time, performance percentage, and performance classification into four categories: Very High, High, Medium, and Low. Furthermore, usability evaluation using the USE method yielded excellent results, with an average score of 88.16%.]]>
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