Implementation of Recurrent Neural Network with Long Short Term Memory Algorithm for Dengue Fever Prediction in Medan City - Rivani Kabrina Br Surbakti; Rima Aprilia; R Maisaroh Rezyekiyah Siregar (Journal of Mathematics, Computations and Statistics, 2026)
. Dengue Hemorrhagic Fever (DHF) remains one of the major public health problems in Medan City due to the high incidence rate each year. Accurate prediction of DHF cases is essential as an early warning and to support health policy planning. This study aims to implement the Recurrent Neural Network (RNN) with the Long Short-Term Memory (LSTM) algorithm to predict the number of DHF cases in Medan City. The data used consist of monthly DHF cases from each public health center (puskesmas) in Medan City from January 2020 to December 2024, obtained from the Medan City Health Office. The data were preprocessed through normalization and divided into training and testing sets. The LSTM model was developed with several testing scenarios of units, epochs, and batch size, and evaluated using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the LSTM model could predict DHF cases with relatively low error rates, achieving an RMSE of 2.02 and an MAE of 1.64 at the best configuration. Therefore, it can be concluded that the LSTM algorithm is effective in predicting the number of DHF cases in Medan City and can serve as a reference in prevention and disease control strategies. Keywords: Dengue Hemorrhagic Fever (DHF), Prediction, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Time Series.