<![CDATA[Dengue fever (DHF) is an infectious disease caused by the dengue virus and transmitted through the bite of the Aedes aegypti mosquito. This disease is a major health problem in many tropical countries, including Indonesia. Identification and classification of DHF patients is very important to prevent further spread and to provide appropriate medical treatment. In this study, the classification of DHF disease is carried out using the K-Means algorithm, which is one of the methods in machine learning used to classify data based on similarity of features. This study aims to apply the K-Means algorithm in classifying DHF cases based on data on symptoms that appear in patients, such as high fever, joint pain, skin rashes, and others. The data used includes patient medical records that record various clinical and demographic parameters. The K-Means algorithm is used to group the data into clusters that describe the severity category or potential risk of dengue disease. The results showed that the K-Means algorithm can be used to cluster DHF patients well, with the division of groups that can describe the severity of the disease. Evaluation was conducted using metrics such as silhouette and cluster validity to assess the effectiveness of the algorithm in performing classification. This model is expected to help medical personnel in decision-making, provide early warning, and improve rapid response to dengue cases.]]>
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