SPATIAL MODELING OF SCHOOL DROPOUT RATES IN UNDERDEVELOPED AREAS OF PAPUA USING GEOGRAPHICALLY WEIGHTED REGRESSION - Al Azies, Harun; Brilianti Rochmanto, Hani; Fitriani, Fenny (Jurnal Informatika dan Teknik Elektro Terapan, 2025)
This study examines the factors hypothesized to contribute to school dropout rates in disadvantaged regions of Papua Province and explores potential geographical influences. The primary aims are to derive parameter estimates and statistical tests for the model of underdeveloped regions in Papua using Geographically Weighted Regression (GWR) and to determine the factors influencing school dropout rates in these areas, providing a basis for governmental policy development to mitigate school dropout issues in disadvantaged regions. Findings reveal that the highest dropout rates occur at the junior high school level, with indications of spatial clustering in dropout cases due to heterogeneity among observation sites. This suggests that regions with elevated dropout rates, or conversely low rates, are likely to have neighboring areas with comparable patterns, necessitating the use of spatial regression modeling with a Fixed Gaussian Kernel function. GWR analysis resulted in two clusters based on significant variables, which include the student-teacher ratio at the junior high school level, the student-classroom ratio at the junior high school level, and the elementary school dropout rate (APTs).