<![CDATA[Football is a very popular sport, and the Indonesian National Team is the pride of the Indonesian people. In an effort to improve team performance, especially in facing the 2026 World Cup qualifiers, optimal player selection is a major challenge. This study applies data mining technology to determine potential players who can strengthen the Indonesian Senior National Team. Player data is taken from the Transfermarkt site which includes attributes such as player market value, club, and league. The methods used include data collection, data cleaning and normalization, and analysis using the K-Means clustering algorithm. The analysis process successfully grouped players into four clusters based on their potential. Players in clusters 1 and 3 have high potential to fill the main lineup, while players in cluster 0 show long-term development prospects. Visualization and manual evaluation support the interpretation of the results for strategic decision making. This study shows that the use of data mining can improve efficiency and accuracy in player selection, providing a more objective data-based approach. However, this study has limitations, such as the lack of consideration of non-technical factors. With the addition of data from other sources and the use of additional algorithms, this method can be further developed to support the performance of the Indonesian National Team optimally in the future.]]>
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