<![CDATA[This paper presents the development of a system designed to detect body and facial movements in boarding houses using clustering methods. The primary goal of the system is to enhance security and monitor the activities within these living spaces. The system utilizes advanced clustering algorithms to accurately identify and classify different types of movements, distinguishing between normal daily activities and potential security threats.The implementation of this system involves the integration of multiple sensors and cameras strategically placed throughout the boarding house. These devices collect data on movements, which is then processed using clustering techniques to identify patterns and anomalies. The clustering method helps in grouping similar movement patterns together, facilitating the detection of unusual activities.This detection system not only contributes to improved safety and security but also provides insights into the behavior and daily routines of the occupants. By analyzing the clustered data, it becomes possible to identify trends and potential issues that may require attention. The findings from this research demonstrate the effectiveness of clustering methods in movement detection and highlight their potential applications in various security and monitoring contexts]]>
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