APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA
Nonparametric Geographically Weighted Regression (NGWR) model is a development of nonparametric regression with geographic weights for spatial data where parameter estimators are local to each observation location. NGWR is used to obtain the best model for the Open Unemployment Rate (OUR) data in In...
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Universitas Pattimura
2023-12-01
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| Series: | Barekeng |
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9460 |
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| author | Marisa Nanda Saputri Sifriyani Sifriyani Wasono Wasono |
| author_facet | Marisa Nanda Saputri Sifriyani Sifriyani Wasono Wasono |
| author_sort | Marisa Nanda Saputri |
| collection | DOAJ |
| description | Nonparametric Geographically Weighted Regression (NGWR) model is a development of nonparametric regression with geographic weights for spatial data where parameter estimators are local to each observation location. NGWR is used to obtain the best model for the Open Unemployment Rate (OUR) data in Indonesia. Unemployment is still a significant social and economic problem in Indonesia. This study aims to obtain the NGWR model on the OUR data in Indonesia and to determine the factors that significantly affect OUR. The method used is the NGWR model with bisquare kernel function weighting and gaussian kernel function. The best model is obtained by NGWR with bisquare kernel function weighting at order 1 and knot point 1, with R2 is 83.45 percent which explains that the predictor variables affect the OUR by that number. The factors that have a significant effect on OUR are the percentage of population density, minimum wage, average years of schooling, GRDP, and the percentage of poor people. |
| format | Article |
| id | doaj-art-2b71f3bb202541dbb433642c558c1b4d |
| institution | DOAJ |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-2b71f3bb202541dbb433642c558c1b4d2025-08-20T03:02:45ZengUniversitas PattimuraBarekeng1978-72272615-30172023-12-011742071208010.30598/barekengvol17iss4pp2071-20809460APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIAMarisa Nanda Saputri0Sifriyani Sifriyani1Wasono Wasono2Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Mulawarman, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, University of Mulawarman, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, University of Mulawarman, IndonesiaNonparametric Geographically Weighted Regression (NGWR) model is a development of nonparametric regression with geographic weights for spatial data where parameter estimators are local to each observation location. NGWR is used to obtain the best model for the Open Unemployment Rate (OUR) data in Indonesia. Unemployment is still a significant social and economic problem in Indonesia. This study aims to obtain the NGWR model on the OUR data in Indonesia and to determine the factors that significantly affect OUR. The method used is the NGWR model with bisquare kernel function weighting and gaussian kernel function. The best model is obtained by NGWR with bisquare kernel function weighting at order 1 and knot point 1, with R2 is 83.45 percent which explains that the predictor variables affect the OUR by that number. The factors that have a significant effect on OUR are the percentage of population density, minimum wage, average years of schooling, GRDP, and the percentage of poor people.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9460nonparametricregressionsplinespatial dataunemployment |
| spellingShingle | Marisa Nanda Saputri Sifriyani Sifriyani Wasono Wasono APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA Barekeng nonparametric regression spline spatial data unemployment |
| title | APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA |
| title_full | APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA |
| title_fullStr | APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA |
| title_full_unstemmed | APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA |
| title_short | APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA |
| title_sort | application of nonparametric geographically weighted regression method on open unemployment rate data in indonesia |
| topic | nonparametric regression spline spatial data unemployment |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9460 |
| work_keys_str_mv | AT marisanandasaputri applicationofnonparametricgeographicallyweightedregressionmethodonopenunemploymentratedatainindonesia AT sifriyanisifriyani applicationofnonparametricgeographicallyweightedregressionmethodonopenunemploymentratedatainindonesia AT wasonowasono applicationofnonparametricgeographicallyweightedregressionmethodonopenunemploymentratedatainindonesia |