BibTex Citation Data :
@article{J.Gauss14709, author = {Rahmah Merdekawaty and Dwi Ispriyanti and Sugito Sugito}, title = {ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI UPAH MINIMUM KABUPATEN/KOTA DI PROVINSI JAWA TENGAH MENGGUNAKAN MODEL SPATIAL AUTOREGRESSIVE (SAR)}, journal = {Jurnal Gaussian}, volume = {5}, number = {3}, year = {2016}, keywords = {Minimum Wages District/City, Worth Living Needs, CPI, GDP, multiple linear regression, spatial dependence, Spatial Autoregressive}, abstract = { Spatial regression is the result of the development of linear regression method, wherein the location or spatial aspects of the analyzed data are also must be considered. The phenomenon that includes spatial data of which is the deployment of a minimum wage. Minimum Wages District/City is a minimum standard that is used by employers to provide wages to employees in its business environment on a district/city in any given year. Minimum Wages District/City is determined by considering the welfare of workers and the state of the local economy. Factors in worker welfare such as Worth Living Needs and the Consumer Price Index (CPI), while one important indicator to determine the economic conditions in the region within a certain time period is Gross Domestic Product (GDP). Modeling the influence of these factors can be determined by using multiple linear regression and spatial regression. Based on the data processing result, there is a spatial dependence in the Minimum Wages District/City variable in Central Java, so Spatial Autoregressive (SAR) method is used in this study. Variables that significantly affect the UMK in Central Java through multiple linear regression method and SAR is the Worth Living Needs (X 1 ) and CPI (X 2 ). The SAR model generates the value of R 2 at 72.269% and AIC at 66.393, better than the multiple linear regression model that generates the value of R 2 at 68% and AIC at 68.482. Keywords : Minimum Wages District/City, Worth Living Needs, CPI, GDP, multiple linear regression, spatial dependence, Spatial Autoregressive }, issn = {2339-2541}, pages = {525--534} doi = {10.14710/j.gauss.5.3.525-534}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/14709} }
Refworks Citation Data :
Spatial regression is the result of the development of linear regression method, wherein the location or spatial aspects of the analyzed data are also must be considered. The phenomenon that includes spatial data of which is the deployment of a minimum wage. Minimum Wages District/City is a minimum standard that is used by employers to provide wages to employees in its business environment on a district/city in any given year. Minimum Wages District/City is determined by considering the welfare of workers and the state of the local economy. Factors in worker welfare such as Worth Living Needs and the Consumer Price Index (CPI), while one important indicator to determine the economic conditions in the region within a certain time period is Gross Domestic Product (GDP). Modeling the influence of these factors can be determined by using multiple linear regression and spatial regression. Based on the data processing result, there is a spatial dependence in the Minimum Wages District/City variable in Central Java, so Spatial Autoregressive (SAR) method is used in this study. Variables that significantly affect the UMK in Central Java through multiple linear regression method and SAR is the Worth Living Needs (X1) and CPI (X2). The SAR model generates the value of R2 at 72.269% and AIC at 66.393, better than the multiple linear regression model that generates the value of R2 at 68% and AIC at 68.482.
Keywords : Minimum Wages District/City, Worth Living Needs, CPI, GDP, multiple linear regression, spatial dependence, Spatial Autoregressive
Article Metrics:
Last update:
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Media Statistika journal and Department of Statistics, Universitas Diponegoro as the publisher of the journal. Copyright encompasses the rights to reproduce and deliver the article in all form and media, including reprints, photographs, microfilms, and any other similar reproductions, as well as translations.
Jurnal Gaussian and Department of Statistics, Universitas Diponegoro and the Editors make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal. In any way, the contents of the articles and advertisements published in Jurnal Gaussian journal are the sole and exclusive responsibility of their respective authors and advertisers.
The Copyright Transfer Form can be downloaded here: [Copyright Transfer Form Jurnal Gaussian]. The copyright form should be signed originally and send to the Editorial Office in the form of original mail, scanned document or fax :
Dr. Rukun Santoso (Editor-in-Chief) Editorial Office of Jurnal GaussianDepartment of Statistics, Universitas DiponegoroJl. Prof. Soedarto, Kampus Undip Tembalang, Semarang, Central Java, Indonesia 50275Telp./Fax: +62-24-7474754Email: jurnalgaussian@gmail.com
Jurnal Gaussian by Departemen Statistika Undip is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Visitor Number:
View statistics
