Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020)

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Comparison of Geographically Weighted Regression Analysis and Global Regression on Modeling the Unemployment Rate in West Java

Authors
Euis Sartika, Anny Suryani
Corresponding Author
Euis Sartika
Available Online 22 December 2020.
DOI
10.2991/aer.k.201221.078How to use a DOI?
Keywords
Unemployment Rate, Geographically Weighted Regression, West Java
Abstract

This study aims to identify the factors Unemployment Rate (UR) in West Java and develop the appropriate model. This study applied the location (spatial) element using Geographically Weighted Regression (GWR). The GWR model was compared with the global regression. The data used in this study are secondary data on 2017 UR for 27 cities/ regencies in West Java. The dependent variable (Y) is the Unemployment Rate (UR), the independent variables include Population Density Level (PDL), Gross Regional Domestic Product(GRDP), Regional Minimum Wage (RMW), Level of Active Labor Participation Rate (ALPR), and Human Development Index (HDI). The results show that the GWR model provides a coefficient of determination (R2) more significant than the global regression model. The Akaike Information Criteria (AIC) value of the GWR model is smaller than the global regression model, meaning that the local regression model of error value is smaller than the global regression model. In other words, the local regression model is better than the global regression model. The factor affecting UR globally is RMW. There are 27 different combinations of local regression models according to the number of cities/regencies in West Java.

Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020)
Series
Advances in Engineering Research
Publication Date
22 December 2020
ISBN
10.2991/aer.k.201221.078
ISSN
2352-5401
DOI
10.2991/aer.k.201221.078How to use a DOI?
Copyright
© 2020, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - CONF
AU  - Euis Sartika
AU  - Anny Suryani
PY  - 2020
DA  - 2020/12/22
TI  - Comparison of Geographically Weighted Regression Analysis and Global Regression on Modeling the Unemployment Rate in West Java
BT  - Proceedings of the International Seminar of Science and Applied Technology (ISSAT 2020)
PB  - Atlantis Press
SP  - 472
EP  - 478
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.201221.078
DO  - 10.2991/aer.k.201221.078
ID  - Sartika2020
ER  -