Article Per Year (5 Year)
p-Index From 2021 - 2026
0.888
P-Index
This Author published in this journals
All Journal Juriti Prima (Jurnal Ilmiah Teknik Industri Prima) Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika JiTEKH (Jurnal Ilmiah Teknologi Harapan) (JIMBE) Bigint Computing Journal Journal of Mathematics, Computation and Statistics (JMATHCOS)
Nasution, Rini Halila
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Public Health

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

 1 
Geographically Weighted Regression Model in the Case of Unemployment in North Sumatra Munawar, Muhammad Arfie; Rahkmawati, Fibri; Nasution, Rini Halila
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/3vd63s64

Abstract

Unemployment is a major and complex issue that affects many aspects of society, particularly in regions such as North Sumatra. This issue is not merely about numbers it also concerns the welfare of the population. Each district or city exhibits varying levels of unemployment; some areas have high rates, while others are relatively low. These variations reflect a clear spatial heterogeneity. To address the significant spatial variation in the factors contributing to unemployment, this study applies the Geographically Weighted Regression (GWR) model to analyze and model unemployment in North Sumatra, taking into account the spatial variability of each independent variable’s influence. GWR is a regression method that allows model parameters to vary across geographic locations, making it possible to capture non-uniform relationships at different spatial points. The methodology involves four weighting functions adaptive Gaussian, adaptive bisquare, fixed Gaussian, and fixed bisquare to identify the most optimal model. The best-performing GWR model is then constructed, and the spatial distribution patterns of unemployment are analyzed. The data used in this study are sourced from official statistics. The results show that the adaptive bisquare GWR model provides the best performance, yielding the lowest AIC value of 130.066. Variables such as population density and population growth rate are significant in most regions. However, number of industries is only significant in certain areas, while total population and minimum wage are not significant. These findings indicate that the factors driving unemployment and their spatial distribution vary across regions, highlighting the importance of considering spatial heterogeneity.
Co-Authors Fadhillah RH, Riezky Hasibuan , Taufik Hidayat Hasibuan, Yetti Meuthia Hisbullah, Riki Munawar, Muhammad Arfie Qosammah, Fairuz Naila Rahkmawati, Fibri Ramayanti, Siti Nova Siboro, Fernando Rikardo Syam, Fachry Tamimi, Alifiyani Utari, Panca Wahyu