<![CDATA[Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus transmitted by the Aedes aegypti mosquito. The spread of DHF is greatly influenced by environmental conditions such as temperature, rainfall, humidity, and population density. In Indonesia, DHF has become a significant public health problem, especially in densely populated urban areas. Therefore, it is important to develop a predictive model that can forecast the potential occurrence of DHF based on environmental variables to reduce the impact and control the spread of this disease. The objective of this research is to develop a predictive model using the Artificial Neural Network Perception (ANN) method to predict the potential occurrence of DHF based on environmental variables, and to create an application for predicting the potential of DHF. This model is expected to help authorities make appropriate decisions to prevent and control DHF outbreaks. The research methodology includes the following stages: data collection, data preprocessing, ANN model development, model evaluation, and implementation and validation. The expected output of this research is an ANN model that can accurately predict the potential occurrence of DHF based on environmental conditions. Additionally, it is hoped that a predictive system will be available for authorities to take effective preventive and control measures against DHF. The research is expected to make a significant contribution to public health, particularly in the prevention and control of DHF. The results include an application for predicting the potential occurrence of DHF in a specific area, with features such as a Dashboard Interface, Temperature Interface, Dataset Interface, and Result Model Interface. The RMSE results obtained for this research were 0.01441372. From the research results, it can be concluded that ANN can be used to predict the potential for dengue fever to enter.]]>
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