Prediksi Jumlah Kasus Penyakit Demam Berdarah Dengue Menggunakan Metode Long Short-Term Memory (LSTM)
DOI:
https://doi.org/10.55606/juitik.v6i1.2100Keywords:
Dengue Hemorrhagic Fever, Disease Surveillance, LSTM, Prediction, Time SeriesAbstract
Dengue Hemorrhagic Fever (DHF) an infectious disease with fluctuating case numbers that can suddenly increase, posing significant public health challenges. In Pontianak City, 106 cases with 1 death were recorded in 2019, decreasing from 195 cases with 3 deaths in 2018. In 2020, the number dropped further to 27 cases with no fatalities. This condition indicates the need for a prediction system capable of accurately estimating the number of cases to support decision-making processes. This study aims to develop a model for predicting daily DHF cases in Pontianak City using the Long Short-Term Memory (LSTM) method. The data used includes daily DHF cases, average temperature, average humidity, and rainfall from 2020 to 2025. The research stages included data cleaning, normalization using Min-Max Scaling, historical data formation, model training, and evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The best model employed a single LSTM layer with 64 neurons, 50 epochs, and a batch size of 32, yielding an RMSE of 0.87 and MAE of 0.63. These results indicate that the LSTM method is capable of generating predictions close to actual values and is reliable for estimating daily DHF cases in Pontianak City. The developed Streamlit-based application provides interactive visualization and accurate predictions, making it a valuable tool for health authorities in DHF prevention and control efforts.
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