<![CDATA[The rapid development of digital technology has led to a significant increase in smartphone usage in modern society. The intensity and diversity of smartphone usage form different behavioral patterns among individuals, resulting in complex data that are difficult to analyze using conventional methods. This study aims to detect and cluster smartphone usage behavior using the K-Means Clustering method. The data used include daily usage duration, application access frequency, and the most frequently used application categories. The research process begins with data preprocessing, normalization, determining the optimal number of clusters using the elbow method, and applying the K-Means algorithm. The clustering results are evaluated using the silhouette score, Davies-Bouldin Index, and inertia. The results show that the K-Means algorithm is able to group smartphone users into 5 (five) clusters with distinct characteristics, namely Heavy User, Light User, Minimal User, Moderate User, and Normal User. The quality of the resulting clusters is considered good with a Silhouette Score of 0.62 (good category), a Davies-Bouldin Index of 0.85 (low value indicating good separation between clusters), and an Inertia value of 1,245.78, which indicates stable compactness of data within clusters. This study is expected to provide a deeper understanding of smartphone usage patterns and serve as a basis for promoting healthier and more controlled smartphone usage.]]>
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