Prediction of Dengue Fever Cases Using the Linear Regression Method Based on Open Data from West Java Province

Muhammad Khysam Firdaus, Herman Yuliansyah

Abstract

Dengue Hemorrhagic Fever (DHF) is a widespread disease in tropical regions, including Indonesia. West Java Province reports the highest number of cases, influenced by factors such as rainfall, population density, and total population. Accurate prediction of DHF cases is essential for effective prevention and control strategies. This study aims to propose a predictive model for DHF cases in West Java using the Linear Regression method and to evaluate its performance using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. The research utilizes secondary data from 2014 to 2023 on DHF cases, population density, and total population from the Open Data Jabar platform. Rainfall data were collected from Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) and Badan Pusat Statistik Indonesia (BPS). The research process includes data collection, preprocessing, time series splitting, model training and iteration, prediction, and performance evaluation. The results show that among the five focus regions, Bandung City achieved the best prediction performance, with a MAPE of 45.82% and an RMSE of 1216.105. These findings indicate that Multiple Linear Regression is reasonably effective for predicting DHF cases, particularly in Bandung. Despite limitations in data availability—especially rainfall data—the model provides informative insights. Future work could improve prediction accuracy by incorporating additional independent variables and more advanced modeling techniques, such as machine learning.

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DOI: https://doi.org/10.37058/innovatics.v7i2.16143

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