Prediksi Risiko Demam Berdarah Dengue Menggunakan Algoritma Random Forest Berbasis GIS di Kota Tangerang
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.1323Keywords:
Dengue Hemorrhagic Fever; Geographic Information System; Risk Prediction; Random Forest; Spatial Cross-ValidationAbstract
Dengue Hemorrhagic Fever (DHF) remains a major public health challenge in Indonesia, as its transmission is influenced by various factors, including climate conditions, environmental characteristics, population density, and spatial characteristics. Early identification of high-risk areas is needed to support more effective dengue prevention and control strategies. This study aims to develop a Geographic Information System (GIS)-based dengue risk prediction model using the Random Forest algorithm in Tangerang City. The study utilized secondary data consisting of historical dengue case records obtained from the Tangerang City Health Office, rainfall, temperature, humidity, healthcare facilities, and administrative boundary data for the 2023–2025 period. The dengue case data consisted of recorded cases from the Tangerang City Health Office and underwent completeness and consistency checks. The research process included data preprocessing, feature engineering, spatial data integration, model development, and evaluation using Spatial Cross-Validation to reduce potential bias caused by spatial autocorrelation and assess the model's generalization capability. The time-based evaluation resulted in an Area Under the Curve (AUC) of 0.973 and an F1-score of 0.889, while the Spatial Cross-Validation evaluation produced an AUC of 0.956 and an F1-score of 0.892. These results indicate that the model maintained good performance when evaluated across different geographic areas. In addition, the developed GIS-based dashboard provides interactive visualization of dengue risk distribution to support the identification of priority areas and data-driven decision-making for dengue prevention and control in Tangerang City.
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