Analisis Spasial Angka Kematian Balita di Pulau Papua Menggunakan Mixed Geographically Weighted Regression
Abstract
One of the goals of the Sustainable Development Goals is to end under five mortality which can be prevented by at least 25 per 1000 live births by 2030. Based on Badan Pusat Statistik (BPS) data, in 2020 the Under Five Mortality Rate (U5MR) in Papua Province is 49.04, while in West Papua Province of 47.23. This figure makes the island of Papua the island with the highest U5MR compared to other islands in Indonesia. The problem of U5MR has different influencing factors for each region, so it is important to include spatial effects in the analysis. The Mixed GWR model can be used to overcome spatial linkages between regions, accommodate variations in the form of spatial heterogeneity, and handle variations in parameters that are global and local in nature. Therefore, this study aims to analyze the variables that affect U5MR in Papua Island using Mixed GWR. This study uses secondary data sourced from BPS. The unit of analysis for this research is the districts/cities in Papua Island. The dependent variable in this study is U5MR, while the independent variables include the percentage of women aged at first pregnancy less than 21 years, Gross Regional Domestic Product per capita, the percentage of households with the main type of fuel in the form of solid fuel, the average length of schooling, and the percentage of households with access to source of proper drinking water. The results showed that the percentage of women aged at first pregnancy less than 21 years, the percentage of households with the main type of fuel in the form of solid fuel, the average length of schooling, and the percentage of households with access to source of proper drinking water had a significant effect on U5MR in several districts/cities on the island of Papua. Therefore, it is hoped that district/city governments on the island of Papua in developing programs/policies to reduce U5MR can adjust to the conditions of each region.
Keywords: Under Five Mortality Rate; Papua island; Mixed GWR
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Melani, Natalia dan Atik Nurwahyuni, “Analisis Faktor yang Berhubungan dengan Demand atas Pemanfaatan Penolong Persalinan di Provinsi Banten: Analisis Data Susenas 2019”, Jurnal Inovasi Penelitian, no. 2, pp. 10, 2022.
Badan Pusat Statistik, Angka Kematian Balita Per 1000 Kelahiran Hidup Menurut Provinsi, 2020.
Badan Pusat Statistik, Profil Kesehatan Ibu dan Anak 2022, 2022.
Kementrian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional, Peta Jalan SDGs Indonesia Menuju 2030, 2021.
Badan Pusat Statistik, Angka Kematian Balita/AKBa (Under Five Mortality Rate/U5MR) Hasil Long Form SP2020 Menurut Provinsi, 2023.
Dewi, I. S, & Nursiyono, J. A, “Faktor-Faktor yang Memengaruhi Kematian Balita di Jawa Timur Tahun 2020 Menggunakan Geographically Weighted Regression (GWR)”, Jurnal Ilmiah Komputasi dan Statistika, vol. 2, no. 2, pp. 32-39, 2023.
Wuryanti I. F, Purnami S.W, Purhadi, “Pemodelan Mixed Geographically Weighted Regression (MGWR) pada Angka Kematian Balita di Kabupaten Bojonegoro Tahun 2011”, Jurnal Sains dan Seni pomits, vol. 2, no. 1, pp. 2337-3520, 2013.
Zeng. C, Yang. L, Zhu. A. X, Rossiter. D. G, Liu. J, & Wang. D, “Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method”, Geoderma, pp. 69-82, 2016.
Fotheringham. A. S, Brunsdon. C, & Charlton. M, Geographically weighted regression: the analysis of spatially varying relationships, UK: John Wiley & Sons, 2002.
Jarque. C. M, & Bera. A. K, “A test for normality of observations and regression residuals”, International Statistical Review/Revue Internationale de Statistique, pp. 163-172, 1987.
Mansfield. E. R, & Helms. B. P, “Detecting multicollinearity”, The American Statistician, vol. 36, no. 3a, pp. 158-160, 1982.
Breusch. T. S, & Pagan. A. R, “A simple test for heteroscedasticity and random coefficient variation”, Econometrica: Journal of the econometric society, pp. 1287-1294, 1979.
Hocking. R. R, Methods and Applications of Linear Models Regression and the Analysis of Variance (2nd ed.), Canada: John Wiley & Sons, 2003.
Azizah I, Handayani OK, “Kematian Neonatal di Kabupaten Grobogan”, Higeia J Public Heal Res Dev, vol. 1, no. 4, pp. 72-85, 2017.
Abbuy. E. K, “Macroeconomic Determinants of Infant Mortality in WAEMU Countries: Evidence from Panel Data Analysis”, Applied Economics and Finance, vol. 5, no. 6, pp. 52-60, 2018. https://doi.org/10.11114/aef.v5i6.3682.
Dherani. M, Pope. D, Mascarenhas. M, Smith. K. R, Weber. M, & Bruce. N, “Indoor air pollution from unprocessed solid fuel use and pneumonia risk in children aged under five years: a systematic review and meta-analysis”, Bulletin of the World Health Organization, pp. 390-398C, 2008.
Woldeamanuel. B.T, “Socioeconomic, Demographic, and Environmental Determinants of Under-5 Mortality in Ethiopia : Evidence from Ethiopian Demographic and Health Survey”, 2019.
Kaldewei. Cornelia, “Determinants of Infant and Under-Five Mortality-The Case of Jordan”, 2010.
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