Analisis Pengaruh Jumlah Penduduk terhadap Insidensi Demam Berdarah Dengue di Provinsi Nusa Tenggara Barat: Pendekatan Regresi Linear Sederhana dan Implikasinya terhadap Kebijakan Pembangunan Daerah

Rian Aditia; Hisbullah; Hanif  Al Jauziah

doi:10.70716/alpha.v2i2.487

Authors

Rian Aditia Universitas Islam Al Azhar Mataram
Hisbullah Universitas Bumigora
Hanif Al Jauziah Universitas Bumigora

DOI:

https://doi.org/10.70716/alpha.v2i2.487

Keywords:

Dengue Hemorrhagic Fever , Population size, Simple linear regression, Regional development policy, West Nusa Tenggara.

Abstract

Dengue Hemorrhagic Fever (DHF) remains a major vector-borne public health problem in Indonesia, including West Nusa Tenggara Province (NTB). Beyond its epidemiological consequences, dengue also affects household economic burden, community productivity, and regional health expenditure. This study aims to analyze the effect of population size on dengue incidence in NTB and to formulate its implications for regional development policy. A quantitative approach was applied using simple linear regression. The study used secondary data on population size and dengue cases from ten districts/cities in NTB during the 2018-2023 period, resulting in 60 observations. Population size was treated as the independent variable, while the number of dengue cases was used as the dependent variable. The analysis showed a positive and statistically significant linear relationship between population size and dengue incidence, with the regression equation Y = -66.384 + 0.0037X. The ANOVA test produced an F-value of 199.284 with p < 0.001, while the coefficient of determination calculated from the model sum of squares yielded an R² of 0.775. These findings indicate that population size explains approximately 77.5% of the variation in dengue cases within the simple model. The results highlight the importance of integrating demographic information into health planning, environmental control, sanitation improvement, and evidence-based regional development policy. Nevertheless, further studies should incorporate additional variables such as population density, rainfall, temperature, humidity, sanitation quality, and socioeconomic factors to obtain a more comprehensive epidemiological understanding.

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