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Dengue Hemorrhagic Fever (DHF) remains a major public health problem in Indonesia, particularly in densely populated areas. Control efforts require accurate data and spatial analysis to understand disease distribution patterns. Geographic Information System (GIS) is an effective tool for visualizing case distribution and supporting surveillance and planning of control programs at the primary healthcare level. This study aims to describe the spatial distribution of Dengue cases based on medical record data and produce a geographic distribution map to support Dengue control efforts at the Puskesmas level. This study used a quantitative descriptive design with secondary data from medical records at Karangsari Health Center. The sample consisted of 255 DHF patients in 2025, selected using a total sampling technique. Data were processed through editing, geocoding patient addresses, and spatial analysis using QGIS software.The results showed 255 Dengue  cases in 2025 with fluctuating monthly trends, peaking in April and lowest in December. Case distribution was uneven and tended to cluster. High-risk areas accounted for 15.7%–21.2%, moderate-risk areas 9.8%–15.7%, and low-risk areas 7.1%–9.8%. Megu Cilik Village had the highest proportion of cases, while other villages were categorized as moderate and low risk. This pattern indicates that Dengue incidence is influenced by environmental conditions, vector density, host factors, rainfall, and Aedes aegypti presence. GIS provides clearer spatial visualization, helping identify high-risk areas and supporting targeted public health interventions.

Dengue fever is a disease commonly found in tropical and subtropical regions. This disease can cause severe symptoms, such as very high fever, accompanied by nausea, vomiting, headache, abdominal pain, and leukopenia (decrease in white blood cells). This infectious disease, known as dengue hemorrhagic fever (DHF), is a viral infection transmitted by the Aedes Aegyppti mosquito. This study aims to classify dengue-prone areas using the K-Means Algorithm, and to classify the factors that cause dengue in Purwodadi District, Grobogan Regency. The clustering results using the K-Means algorithm with Rapidminer tool from 266 data produced 3 clusters: cluster 0 (blue) with 138 patients dominated by Kuripan, Purwodadi, Ngambak villages, cluster 1 (green) with 31 patients in Ngraji, Nambuhan, Cingkrong villages, and cluster 2 (orange) with 97 patients in Danyang, Kalongan, Pulorejo villages. This study is expected to provide additional information for stakeholders in controlling dengue cases and increase awareness of the importance of environmental cleanliness as a preventive measure.

Dengue Hemorrhagic Fever (DHF) remains a major public health problem in endemic areas, including Olat Rarang Hamlet, Labuhan Sumbawa Village. Effective dengue prevention requires active community involvement supported by entomological surveillance data. This community service activity aimed to enhance community awareness and participation in dengue prevention through the implementation of the 3M Plus Movement integrated with an entomological surveillance approach using the House Index (HI), Container Index (CI), and Breteau Index (BI) indicators. The activity was conducted on December 6, 2025, involving local community members and public health students. The methods included health education, larval surveys in houses and water-holding containers, and community-based mosquito breeding site eradication. The results indicated that houses and containers positive for Aedes larvae were still present, reflecting a moderate risk of dengue transmission. However, the implementation of the 3M Plus Movement improved community knowledge, awareness, and participation in vector control. Integrating the 3M Plus Movement with entomological surveillance is an effective promotive and preventive strategy based on community empowerment for sustainable dengue prevention.