Comparative Performance of LSTM and SARIMAX for Monthly Dengue Fever Forecasting in Sukabumi City

Abstract

Dengue Fever (DF) remains one of the most significant vector-borne diseases in Indonesia, including Sukabumi City, where the number of reported cases exhibits strong seasonal fluctuations influenced by climatic and demographic factors. Accurate forecasting is therefore essential to support early prevention and public health decision-making. This study compares the predictive performance of Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) for forecasting monthly dengue cases in Sukabumi City. The forecasting models incorporate rainfall, temperature, humidity, and population density as predictor variables. The study follows the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset comprises 96 monthly observations collected between 2018 and 2025 and is divided into training and testing sets using an 80:20 ratio. Model performance is evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that the SARIMAX model outperforms LSTM, achieving an MAE of 19.50 and a MAPE of 19.54%, whereas LSTM records an MAE of 20.96 and a MAPE of 21.13%. These findings indicate that SARIMAX is more suitable for forecasting dengue incidence characterized by strong seasonal patterns and relatively limited observations. The proposed comparison provides practical insights for selecting appropriate forecasting models to support evidence-based dengue prevention and public health planning in Sukabumi City.