THE IMPLEMENTATION OF GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) METHOD ON OPEN UNEMPLOYMENT RATE IN REGENCY/CITY OF SUMATRA ISLAND

  • Syarifah Meurah Yuni Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Syiah Kuala, Indonesia https://orcid.org/0009-0003-7518-5521
  • T. Murdani Saputra Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Syiah Kuala, Indonesia https://orcid.org/0000-0001-7753-5327
  • Nadya Nur Fadhilah Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Syiah Kuala, Indonesia
DOI: https://doi.org/10.30598/barekengvol19iss1pp73-86
Keywords: Fixed Gaussian Kernel, Geographically Weighted Regression, Open Unemployment Rate, Weighted Least Square

Abstract

Unemployment is a condition where a person who is included in the labor force but does not have a job and is not actively looking for work. The number of unemployed is measured using the Open Unemployment Rate (OUR) indicator. OUR is obtained by comparing the number of job seekers and the number of labor force. This study aims to obtain a model of OUR in each district / city of Sumatra Island and what factors influence it using the Geographically Weighted Regression (GWR) method and Fixed Gaussian Kernel Function weighting, and describe predictor variables on thematic maps. The GWR method is one of the statistical methods that can prevent the presence of spatial aspects in the data. The parameters estimated by the local regression model vary at each location point and are estimated using the Weighted Least Square (WLS) method. Based on the research results obtained from this study, the GWR models obtained amounted to 154 different local models in each district / city on the island of Sumatra. Variables Labor Force Participation Rate, Population Growth Rate, Population Density and Average Years of Schooling have a significant influence on each location, meanwhile variable Percentage of Poor Population and variable Poverty Line have no influence on any location. These variables are able to explain the OUR by 57.2%, where the remaining 42.8% is explained by other factors that are not explained in the model.

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Published
2025-01-13
How to Cite
[1]
S. M. Yuni, T. M. Saputra, and N. N. Fadhilah, “THE IMPLEMENTATION OF GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) METHOD ON OPEN UNEMPLOYMENT RATE IN REGENCY/CITY OF SUMATRA ISLAND”, BAREKENG: J. Math. & App., vol. 19, no. 1, pp. 73-86, Jan. 2025.
Issue
Vol 19 No 1 (2025): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2025 Syarifah Meurah Yuni, T. Murdani Saputra, Nadya Nur Fadhilah

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