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All Journal Enthusiastic : International Journal of Applied Statistics and Data Science
Nikmah, Rohmatun
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

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Utilizing Geographically Weighted Regression with a Gaussian Kernel to Analyze Unemployment Hayati, Ma'rufah; Madonna, Nora; Simanjuntak, Erica Grace; Nikmah, Rohmatun
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art3

Abstract

Unemployment is a major challenge in economic development, reflecting an imbalance between labor supply and available job opportunities. This study aimed to examine the spatial variation of factors influencing the open unemployment rate (OUR) in Lampung Province, Indonesia, and to compare the performance of a global regression model with the geographically weighted regression (GWR) model in explaining these variations. The GWR method, using a fixed Gaussian kernel, was applied to capture spatial heterogeneity across regions. Secondary data were obtained from the Statistics Indonesia of Lampung Province in 2023, including economic growth (EG), human development index (HDI), and labor force participation rate (LFPR). The results showed that in the global regression model, LFPR was the only variable that significantly reduced unemployment, while EG and HDI were not statistically significant. The Breusch–Pagan test confirmed spatial heterogeneity, supporting the use of the GWR. The GWR model performed better, with Akaike information criterion (AIC) of 40.8262 and R² of 0.6059. Spatial analysis indicated that EG and HDI positively affected unemployment in several districts, suggesting limited job absorption and possible skill mismatches, whereas LFPR consistently showed a negative relationship with the open unemployment rate (OUR) across regions.
Co-Authors Hayati MT, Ma’rufah Madonna, Nora Simanjuntak, Erica Grace