<![CDATA[Dengue Hemorrhagic Fever (DHF) is one of the major health problems in Indonesia, especially in densely populated urban areas such as Purwokerto. This study aims to develop a Machine Learning (ML)-based DHF risk prediction model integrated with a Geographic Information System (GIS) to support public health preparedness. The data used came from DBD cases reported by the Banyumas District Health Office and climate parameters (air temperature, humidity, rainfall) from the BMKG for the period 2022–2024. Three Machine Learning (ML) algorithms were applied, namely Naive Bayes, Logistic Regression, and Support Vector Machine (SVM). The analysis process included data pre-processing, training and testing data division using stratified sampling techniques, and hyperparameter optimization using Grid Search Cross-Validation. Performance evaluation was conducted using accuracy, precision, recall, and F1-score calculated through a confusion matrix. The results showed that SVM provided the best performance with an accuracy of 0.82, precision of 0.80, recall of 0.84, and F1-score of 0.82, surpassing Logistic Regression with an accuracy of 0.76 and Naive Bayes with an accuracy of 0.71. The integration of classification results with GIS produced a spatial map of DHF risk zones that facilitated the identification of priority areas for intervention. This study contributed by presenting a multi-algorithm ML and GIS-based predictive model using local epidemiological data, which has the potential to improve the effectiveness of DHF mitigation and early detection in Indonesian cities.]]>
Copyrights © 2026
