<![CDATA[Dengue hemorrhagic fever (DHF) is a disease that is commonly found in most tropical and subtropical regions, especially Southeast Asia, including Indonesia, one of which is Sikka Regency, NTT Province. In 2024, there were 821 cases of DHF recorded and at the beginning of the year until February 2025 there were 50 cases of DHF. To overcome the spread of this disease, an early warning system is needed that is able to identify its distribution patterns quickly and accurately. The purpose of this study is to build a mobile application as an early warning system with a Support Vector Machines (SVM) algorithm model based on geospatial data to inform the public and authorities about the potential for a DHF outbreak in Sikka Regency, so that preventive measures can be taken immediately. The SVM model was chosen because of its ability to classify data and is known as one of the classification methods that has high results in predicting potential classifications in data. The geospatial data used includes rainfall, temperature, and humidity data, population density, and the history of DHF cases in Sikka Regency. The results show that the SVM-based approach can improve prediction accuracy compared to conventional methods and provide an innovative solution for dengue fever mitigation through the use of geospatial-based technology.]]>
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