Analisis Spasial Faktor Kematian Maternal Di Jawa Timur Menggunakan Geographically Weighted Regression (GWR)
DOI:
10.29303/jm.v8i2.12146Published:
2026-06-13Downloads
Abstract
Abstract
Maternal mortality is one of the key indicators in assessing public health status and the quality of maternal health services. East Java Province still exhibits considerable variation in maternal mortality rates across districts and cities, necessitating an analytical approach that accounts for regional characteristic differences. This study aims to analyze the factors influencing maternal mortality rates in East Java Province using the Geographically Weighted Regression (GWR) method. Secondary data from 2024 covering 38 districts and cities in East Java were used. The predictor variables analyzed include K1 coverage, K4 coverage, postpartum maternal health services, deliveries assisted by health professionals, vitamin A supplementation, Fe3 tablet distribution, as well as the ratio of hospitals and community health centers. The results indicate that the GWR model with an adaptive Tricube kernel and an optimum bandwidth of 35 is the best-fitting model, with an AICc value of 261.175 and R² of 0.8646, outperforming the OLS model which yielded an R² of 0.6612. Local parameter estimation results reveal that the influence of predictor variables differs across districts and cities, indicating that the factors affecting maternal mortality are spatially varying and heterogeneous across regions.
Keywords: Maternal Mortality, Geographically Weighted Regression, Spatial Analysis, East Java.
Keywords:
Maternal Mortality Geographically Weighted Regression, Spatial Analysis East JavaReferences
Ayu, Y., Qomari, N., Notobroto, H. B., Epidemiologi, D., Kependudukan, B., Kesehatan, P., Masyarakat, F. K., & Airlangga, U. (2022). Kunjungan Ibu Hamil K1 Dan K4 Terhadap Angka Kematian Ibu Di Provinsi Jawa Timur. 13, 586–595. https://doi.org/10.22487/preventif.v13i4.309
Comber, A., Brunsdon, C., Charlton, M., Dong, G., Harris, R., Lu, B., Lü, Y., Murakami, D., Nakaya, T., & Wang, Y. (2023). A route map for successful applications of geographically weighted regression. Geographical Analysis, 55(1), 155–178. https://doi.org/10.1111/gean.12316
Comber, L., & Brunsdon, C. (2020). Geographical data science and spatial data analysis: an introduction in R. Sage. https://doi.org/10.4135/9781526485465
Fotheringham, A. S., Oshan, T. M., & Li, Z. (2023). Multiscale geographically weighted regression: Theory and practice. CRC Press. https://doi.org/10.1201/9781003435464
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: with applications in R (Vol. 103). Springer. https://doi.org/10.1007/978-1-4614-7138-7
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). Linear model selection and regularization. In An introduction to statistical learning: with applications in R (pp. 225–288). Springer. https://doi.org/10.1007/978-1-0716-1418-1_6
James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). Statistical learning. In An introduction to statistical learning: With applications in Python (pp. 15–67). Springer. https://doi.org/10.1007/978-3-031-38747-0_2
Kartiningrum, E. D., & Notobroto, H. B. (2022). Determinan Kematian Ibu Di Propinsi Jawa Timur Pada Masa Pandemi Covid 19. 14(1Kartiningrum, Eka Diah, and Hari Basuki Notobroto. 2022. “HOSPITAL MAJAPAHIT Vol 14 No . 1 Pebruari 2022 HOSPITAL MAJAPAHIT.” 14(1):90–102.), 90–102. https://doi.org/10.55316/hm.v14i1.772
Lessani, M. N., & Li, Z. (2024). SGWR: similarity and geographically weighted regression. International Journal of Geographical Information Science, 38(7), 1232–1255. https://doi.org/10.1080/13658816.2024.2342319
Mailanda, R., & Kusnandar, D. (2022). Analisis autokorelasi spasial kasus positif Covid-19 menggunakan Indeks Moran dan LISA. Bimaster: Buletin Ilmiah Matematika, Statistika Dan Terapannya, 11(3). https://doi.org/10.26418/bbimst.v11i3.55447
Mikolajczyk, A. P., Fortela, D. L. B., Berry, J. C., Chirdon, W. M., Hernandez, R. A., Gang, D. D., & Zappi, M. E. (2024). Evaluating the suitability of linear and nonlinear regression approaches for the Langmuir adsorption model as applied toward biomass-based adsorbents: testing residuals and assessing model validity. Langmuir, 40(39), 20428–20442. https://doi.org/10.1021/acs.langmuir.4c01786
Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.
Muna, M. L. L., Sutiningsih, D., Fauzi, M., & Dharmawan, Y. (2025). Geographically weighted regression (GWR) analysis of factors related to dengue Hemorrhagic fever in central java province, Indonesia. Mosquito Journal, 12(2), 19–24. https://www.dipterajournal.com/pdf/2025/vol12issue2/PartA/12-1-11-810.pdf
Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., & Fotheringham, A. S. (2019). mgwr: A Python implementation of multiscale geographically weighted regression for investigating process spatial heterogeneity and scale. ISPRS International Journal of Geo-Information, 8(6), 269. https://doi.org/10.3390/ijgi8060269
Suarayasa, K. (2020). Strategi menurunkan angka kematian ibu (AKI) di Indonesia. Deepublish. https://opac.perpusnas.go.id/DetailOpac.aspx?id=1301122
Sumarmi, S. (2017). Model Sosio Ekologi Perilaku Kesehatan dan pendekatan Continuum of Care untuk menurunkan angka kematian ibu. The Indonesian Journal of Public Health, 12(1), 129. https://doi.org/10.20473/ijph.v12i1.2017.129-141
Yu, H., Fotheringham, A. S., Li, Z., Oshan, T., Kang, W., & Wolf, L. J. (2020). Inference in multiscale geographically weighted regression. Geographical Analysis, 52(1), 87–106. https://doi.org/10.1111/gean.12189
License
Copyright (c) 2026 Agus Mutia, Siti Alfiatur Rohmaniah, Awawin Mustana Rohmah
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
