<![CDATA[Extracurricular activities play an important role in supporting students' academic and non-academic development. However, differences in student involvement levels often make it difficult for schools to identify participation patterns and provide appropriate evaluation strategies. This study aims to analyze and classify student participation patterns in extracurricular activities using the K-Means clustering algorithm at Nurul Hidayah Islamic Elementary School. The research applies a quantitative approach by utilizing student participation data collected from extracurricular activity records. The data were processed through several stages, including data preparation, normalization, determination of the optimal number of clusters, and implementation of the K-Means algorithm to group students based on their participation characteristics. The results of the clustering process provide several student participation groups with different involvement levels, allowing schools to identify active, moderate, and low participation patterns. The findings indicate that the K-Means algorithm can be applied as an analytical approach to support data-driven evaluation of extracurricular activities. This research is expected to assist schools in developing more targeted strategies to increase student engagement and optimize extracurricular program management.]]>
Copyrights © 2026
