Analisis Angka Kematian Bayi Di Provinsi Nusa Tenggara Timur Dengan Model Regresi Spasial - Elisabeth Brielin Sinu; Ambrosius Dedi A. Sinu (Jurnal Kajian dan Penelitian Umum, 2023)
This research aims to examine the significant factors influencing Infant Mortality Rate (IMR) in the East Nusa Tenggara Province. Estimation is carried out using a spatial regression model approach. The variables under investigation are the Infant Mortality Rate (Y), the percentage of Low Birth Weight (X1), the percentage of infants receiving breastfeeding (X2), and the percentage of deliveries assisted by medical personnel (X3). The research data consist of secondary data from the year 2022 in 22 regencies/cities obtained from the Central Statistics Agency (BPS) of the East Nusa Tenggara Province. Modeling with Ordinary Least Squares (OLS) regression produces one significant independent variable at α=5%, namely the percentage of deliveries assisted by medical personnel. Based on diagnostic tests, spatial dependence occurs at lag, indicating that the appropriate spatial regression model is the Spatial Autoregressive Model (SAR). However, a Spatial Error Model (SEM) is still used as a comparison. From these two spatial models, it is found that the significant independent variable affecting the IMR in the 22 regencies/cities in East Nusa Tenggara is the percentage of deliveries assisted by medical personnel. The weight used is queen contiguity. Based on R2 and AIC criteria, the best spatial regression model is the Spatial Autoregressive Model (SAR) because it has the highest R2 of 0.778282 and the smallest AIC of 132.518. For further research, it is recommended to consider local factors that may influence IMR, such as access to clean water, sanitation, educational level, electrification ratio, which may vary in each region.