<![CDATA[This study aims to group students' level of learning interest based on the intensity of playing online games using the K-Means Clustering algorithm. The issue being addressed is the increasing activity of gaming, which can potentially affect students' learning behavior, but there hasn't been a structured mapping of student characteristics yet. The data used comes from questionnaires filled out by 65 respondents with 6 main variables, including playing frequency, playing duration, playing time, and learning interest indicators. The method used is K-Means with steps of preprocessing, normalization using StandardScaler, and testing the number of clusters using the Elbow method. The study results show that the optimal number of clusters is 3, with a silhouette score of 0.323 and a Davies-Bouldin Index of 1.465. It produced three groups, namely: (1) low learning interest, (2) medium learning interest, and (3) high learning interest. The clustering results showed that the majority of students were in the Medium Learning Interest category with 37 students (56.92%), followed by Low Learning Interest with 16 students (24.62%), and High Learning Interest with 12 students (18.46%). The silhouette score yielded a value of 0.323303, indicating a fairly good cluster structure. This study shows that most students are in a medium condition, meaning they still play games without significantly affecting their learning interest. The contribution of this research is providing a data-based approach to categorize students' learning interest levels related to online gaming activities, and it also serves as a basis for schools to design more effective monitoring and educational strategies. The research provides a mapping of student characteristics based on data that can be used as a basis for making decisions in academic guidance.]]>
