<![CDATA[The rapid development of Wi-Fi networks has become a key pillar in supporting the internet needs of modern society. However, the increasing number of users and their diverse activity patterns pose challenges in network management, especially with regard to bandwidth allocation and service quality. Variations in activity patterns, such as social media, streaming, and gaming, create different bandwidth requirements for each user group. This imbalance in resource utilization can result in degraded quality of service, especially during peak hours. This research aims to address these challenges by clustering Wi-Fi users based on their activity patterns using the K-Means algorithm. The data used includes access time, usage duration, connection intensity, and user activity type. After going through the analysis process, users are grouped into several clusters based on the similarity of activity patterns. The clustering results show significant differences between light, medium, and heavy users in bandwidth consumption and duration of use. The results of this study contribute to more efficient Wi-Fi network management, especially in optimizing bandwidth allocation and supporting data-based decision-making. With customized management strategies for each user group, the quality of service can be significantly improved, providing a better experience for Wi-Fi users.]]>
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