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All Journal Zero : Jurnal Sains, Matematika, dan Terapan
Anggraini, Dewi
Department of Statistics, Lambung Mangkurat University, Banjarbaru
Agriculture, Biological Sciences & Forestry Chemistry Mathematics Physics

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LASSO Quantile Regression for Predictive Modeling of Dengue Hemorrhagic Fever Incidence in Indonesia Arifin, Samsul; Anggraini, Dewi; Susanti, Dewi Sri
ZERO: Jurnal Sains, Matematika dan Terapan Vol 9, No 1 (2025): Zero: Jurnal Sains Matematika dan Terapan
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/zero.v9i1.24877

Abstract

Dengue Hemorrhagic Fever (DHF) is an endemic disease that continues to burden public health in Indonesia, characterized by an uneven pattern of distribution influenced by various environmental, social, and economic factors. This study aims to develop a predictive model for DHF incidence using the LASSO Quantile Regression approach, which can reveal the influence of predictor variables across different quantiles while addressing multicollinearity and overfitting issues. The data used includes nine predictor variables obtained from BPS and BMKG for the year 2025. The estimation results show that the urban/rural Area Size consistently affects all quantiles, while the percentage of population living in poverty and the number of healthcare facilities are significant only at the 0.25 and 0.50 quantiles. Model evaluation indicates that this approach provides good predictive performance, especially at the 0.25 quantile, with a R² pseudo value of 0.2838. These findings suggest that the LASSO Quantile Regression method is effective in identifying the determinants of DHF in Indonesia.
Co-Authors Arifin, Samsul Dewi Sri Susanti