<![CDATA[RSGM Baiturrahmah serves as a medical institution that generates a high volume of daily patient records. However, this wealth of data has not been optimally utilized by management as a primary consideration for strategic decision-making. The identified core problem is the absence of comprehensive patient characteristic mapping, which often leads to an uneven distribution of medical resources. To address this critical issue, this study applies advanced data mining techniques using the K-Means Clustering algorithm to group 1,708 dental and oral disease patient records. The clustering process was conducted by determining three main clusters based on three crucial attributes patient age, the total number of diagnoses received, and the duration of medical service provided. The results of this study successfully classify all patients into three specific service categories, namely Basic Service, Intermediate Service, and Intensive Service. This research also produced a comprehensive web-based decision support system developed using the Python programming language and MySQL database. The system is specifically designed to assist the management of RSGM Baiturrahmah in accurately visualizing the characteristics of each patient group. With the successful implementation of this system, hospital management can be more effective in formulating highly personalized and targeted service strategies for every patient group.]]>
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