Integrasi Faktor Iklim dan Lingkungan untuk Prediksi Risiko DBD di Kota Palembang Menggunakan Pendekatan GeoAI Berbasis LSTM - Tia Arlin Dita; Ali Ibrahim (JURIKOM (Jurnal Riset Komputer), 2026)
Dengue Hemorrhagic Fever (DHF) remains a significant vector-borne disease threat to public health in Palembang. This study aims to analyze the environmental and demographic factors influencing DHF risk and predict risk trends using a GeoAI approach. Four primary variables land surface temperature, rainfall, population density, and residential area were integrated to develop a DHF risk index for the 2020 - 2025 period. The analysis reveals that the risk index consistently falls within the high category across all regions, showing a gradual upward trend from 0.517 in 2020 to 0.527 in 2025. To project future risks, a Long Short-Term Memory (LSTM) model was employed. Model evaluation demonstrated robust performance with a Mean Squared Error (MSE) of 0.0028, a Root Mean Squared Error (RMSE) of 0.052, and a Mean Absolute Error (MAE) of 0.031, indicating low error rates and stable predictive capability. Prediction results suggest that DHF risk is expected to continue increasing through 2029, particularly in sub-districts with high population density and expanding residential areas. This research provides a scientific contribution by developing a predictive model that is more adaptive and precise than conventional statistical approaches. Through the integration of artificial intelligence and spatial data (GeoAI), this model effectively captures non-linear patterns and spatio temporal dynamics, serving as a sustainable early warning system.