Model Geographically Wighted Bivariate Generalized Poisson Regression dengan Adaptive Gaussian Kernel (Studi Kasus: Jumlah Kematian Ibu dan Neonatal di Indonesia)

Frista Delia (Universitas Negeri Gorontalo)
Djihad Wungguli (Universitas Negeri Gorontalo)

Research Review: Jurnal Ilmiah Multidisiplin
Vol. 4 No. 1 (2025): Research Review: Jurnal Ilmiah Multidisiplin (Februari 2025 - Juli 2025)

Maternal Mortality Rate (MMR) and Neonatal Mortality Rate (NMR) are important indicators in determining the level of public health in a region. Both MMR and NMR in Indonesia have decreased, but the reduction has not been significant, and the rates remind high, requiring accelerated efforts to meet the target by the end of 2024. Maternal and neonatal mortality are interrelated, at the nutrition that the baby receives during pregnancy comes from the mother’s body, meaning the mother’s health status significantly influences the health of the newborn. This study aims to identify the factors influencing maternal and neonatal mortality in Indonesia with Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method, using Adaptive Gaussian Kernel weighting. The results results indicate that the independent variables that have a significant effect on the number of maternal and neonatal deaths at each location are the percentage of health services for pregnant women in K4 (X1) and the percentage of deliveries by health workers (X2).