<![CDATA[Infectious diseases remain a major public health problem in North Sumatra Province. The uneven distribution of infectious diseases across districts and cities has resulted in suboptimal disease control efforts that are often not well targeted. Several infectious diseases that still require special attention include tuberculosis, leprosy, malaria, and dengue fever. Variations in disease incidence among regions indicate the need for an analytical method capable of describing disease distribution patterns in a structured manner to support regional-based health policy prioritization. This study applies a data mining approach using clustering methods with the K-Means algorithm to group districts and cities in North Sumatra Province based on infectious disease characteristics. The data used include indicators of tuberculosis case detection, tuberculosis treatment success rates, the number of leprosy cases, malaria morbidity rates, and dengue fever morbidity rates. The study area covers Tapanuli Tengah, Toba Samosir, Labuhanbatu, Simalungun, Dairi, Karo, Deli Serdang, Langkat, Nias Selatan, Pakpak Bharat, Serdang Bedagai, Batu Bara, Padang Lawas, and Labuhanbatu Utara. The research stages consist of data preprocessing, determining the number of clusters, distance calculation using Euclidean Distance, and iterative processes until stable clustering results are obtained. The results show that districts and cities in North Sumatra Province can be grouped into three clusters, namely regions with high, medium, and low levels of infectious diseases. This clustering is expected to support decision-making in determining priority areas for infectious disease control by local governments]]>
