<![CDATA[Dengue Fever (DHF) continues to represent a significant public health threat in Indonesia and other tropical regions, with an annual increase in the number of reported cases. The primary aim of this study is to develop a predictive model for DHF by integrating the Bagging technique and the Decision Tree C4.5 algorithm. The goal is to improve prediction accuracy by incorporating key environmental factors such as temperature, humidity, and rainfall. The research adopts a quantitative methodology with a descriptive approach, using publicly available datasets from data.mendeley.com and conducting the analysis using RapidMiner software. The findings of the study demonstrate that the proposed model is highly effective in accurately predicting and classifying DHF cases, achieving significant precision. In addition to this, the model is successful in identifying important patterns and trends linked to the disease's occurrence. These results underscore the efficacy of combining Bagging and Decision Tree C4.5 as a robust tool for detecting and forecasting DHF outbreaks. The research contributes substantially to the field of data-driven prediction models, offering valuable insights for health agencies to develop more effective and proactive strategies for disease prevention. For future research, it is recommended that additional factors such as genetic and medical data be considered, along with the application of triangulation methods to improve the analysis's validity, scope, and overall robustness. This approach would enable a more comprehensive understanding of DHF and its predictive modeling.Keywords: DHF Prediction; Bagging; Decision Tree C4.5; Machine Learning; Data Mining]]>
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