<![CDATA[Dengue fever outbreaks in urban areas of Java, Indonesia, have become a significant public health concern, with increasing frequency due to climate variability and human mobility patterns, where the spread of dengue is influenced by environmental conditions such as temperature and rainfall as well as human movement within urban centers, making an understanding of these factors crucial for effective disease control and prevention. This study aims to simulate and predict dengue fever outbreaks in urban Java using AI-driven models based on climate variability and human mobility data, with the research seeking to identify the key factors that contribute to the transmission dynamics of dengue fever in urban settings and to evaluate the effectiveness of predictive models in managing potential outbreaks. The study employs machine learning techniques, including Random Forest and Artificial Neural Networks, to analyze climate data consisting of temperature, rainfall, and humidity alongside human mobility data collected from mobile phone tracking and demographic information, where the data is processed to create a simulation model of dengue fever transmission that is validated against historical outbreak data. The results show that the AI-driven model successfully simulated dengue fever outbreaks, demonstrating a high correlation between climate conditions, human mobility, and the spread of the disease, and indicating that increased mobility during the rainy season significantly amplified the risk of outbreaks in high-density urban areas. Overall, the findings conclude that AI-driven simulations offer a promising approach to understanding and predicting dengue fever outbreaks in urban Java, and by incorporating climate and mobility data, the model provides valuable insights for early warning systems and targeted public health interventions.]]>
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