Forecasting Dengue Hemorrhagic Fever (DHF) Cases In Semarang City Using Machine Learning Extreme Gradient Boosting (XGBoost) (Journal of Applied Statistics and Data Science, 2025)
Dengue Hemorrhagic Fever (DHF) remains a significant health issue in Semarang City, with the number of cases fluctuating from year to year. To support appropriate policy-making, an accurate forecasting method is required. This study aims to forecast the number of DBD cases based on monthly data from January 2021 to March 2025 using the Extreme Gradient Boosting (XGBoost) algorithm. Data was obtained from the Semarang City Health Department and analyzed based on the gender of the patients. The XGBoost model was chosen for its ability to capture complex patterns in time series data. Model evaluation using the MAE and RMSE metrics showed satisfactory results, with an MAE value of 7.87 and an RMSE of 8.83 for males, and an MAE of 3.50 and an RMSE of 4.42 for females. Forecast results for the period from April to August 2025 indicate that DBD cases among males are likely to remain stable at around 6–7 cases per month, while cases among females are expected to remain steady at approximately 9 cases. These findings suggest that XGBoost is effective for forecasting DBD cases and can serve as a tool for future health policy planning.