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Mujiarti, Eka May
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PEMODELAN GEOGRAPHICALLY WEIGTED REGRESSION PADA ANGKA PARTISIPASI SEKOLAH DI KALIMANTAN BARAT TAHUN 2022 Mujiarti, Eka May; Yundari, Yundari; Huda, Nur'ainul Miftahul
Jurnal Gaussian Vol 13, No 1 (2024): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.13.1.36-47

Abstract

Angka Partisipasi Sekolah" (APS) indicates educational quality in a region, with higher APS reflecting better education. In 2022, APS for SMA/SMK/MA/Paket C in West Kalimantan was 68.72%, a decrease from the previous year. A Geographically Weighted Regression (GWR) approach which considers geographic characteristics in modeling the relationship between response and predictor variables, is used to analyze factors influencing APS in West Kalimantan. This study aims to model APS and identify influencing factors. Initial steps include detecting multicollinearity and spatial heterogeneity, and determining the Euclidean distance and bandwidth value of the weighting function. The study uses fixed and adaptive Gaussian, bisquare, and tricube kernels. GWR model parameters are then estimated, and the best model is chosen based on the smallest Akaike Criterion Information (AIC) value. Results show that the best weight is the adaptive bisquare kernel with the smallest AIC. Key factors influencing APS, with a 99.07% coefficient of determination, include the number of schools, teachers, student-teacher ratio, poverty rate, and PDRB per capita, with the remaining 0.93% influenced by unstudied factors.
Co-Authors Huda, Nur'ainul Miftahul Yundari, Yundari