This research is motivated by the problem of the Kampili Community Health Center in managing medical record data which is still done manually, thus complicating the analysis process and medical decision-making. This study aims to design and build a web-based medical record data clustering system using the K-Means algorithm. This system is expected to assist the community health center in grouping patient data based on attributes such as disease type, address, gender, age, and number of cases, thereby facilitating the identification of disease patterns. The study uses the Lean Software Development (LSD) development method with data collection techniques in the form of observation, interviews, and literature studies. System testing was conducted using the Blackbox method and the System Usability Scale (SUS). The results showed that the system was successfully implemented and was able to group data effectively, facilitate information retrieval, and produce accurate cluster visualizations. This system obtained a usability score of 84.3 on the SUS scale, which is included in the "Excellent" category with an A rating. This indicates that the application is easy to understand and use by users, and is useful in supporting decision-making in the field of public health.