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APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 4 (2023) · pp. 2071–2080

DOI
10.30598/barekengvol17iss4pp2071-2080
ISSN
2615-3017, 1978-7227
Published
2023-12-19
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

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.

Collaboration network of the authors

40 authors · 87 co-authorship links

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Co-authorOrganisationJoint articles
Andrea Tri Rian DaniAffiliation unknown4
Surya PranggaAffiliation unknown3
Rizki Dwi FadlirhohimAffiliation unknown2
Zakiyah Mar'ahAffiliation unknown2
Meirinda FauziyahAffiliation unknown2
Fidia Deny Tisna AmijayaAffiliation unknown2
Qonita Qurrota A'yunAffiliation unknown2
Eka Nur AmaliahAffiliation unknown1
Darnah DarnahAffiliation unknown1
Ahmad Rizky KesumaAffiliation unknown1
Farikah Ayu RinandaAffiliation unknown1
Ilyas AstafiraAffiliation unknown1
Nur AfrianiAffiliation unknown1
Tri Septi Ayu LestariAffiliation unknown1
Tirta PurnaragaAffiliation unknown1
Rifka Nurfaiza ZarkasiAffiliation unknown1
Aprianti Boma PadatuanAffiliation unknown1
Muhammad Danil PasarellaAffiliation unknown1
Memi Nor HayatiAffiliation unknown1
Yunda Sasha ParadillaAffiliation unknown1
Ar Ruum Mia SariAffiliation unknown1
Mohammad Nurul HudaAffiliation unknown1
Sitti AnggraeniAffiliation unknown1
Frans Karta Sayoga SitohangAffiliation unknown1
Siti MahmudaAffiliation unknown1
Anggi Putri AnisarAffiliation unknown1
Itsar MangngiriAffiliation unknown1
Rahmania RahmaniaAffiliation unknown1
Asnita AsnitaAffiliation unknown1
Syaripuddin SyaripuddinAffiliation unknown1
Shanaz TulzahrahAffiliation unknown1
Rara SuciatiAffiliation unknown1
Indria SaskaAffiliation unknown1
Ghina Fadhilla MumtazAffiliation unknown1
Suyitno SuyitnoAffiliation unknown1
Ghony N NurhudaAffiliation unknown1
Darnah Andi NoheAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">APPLICATION OF NONPARAMETRIC GEOGRAPHICALLY WEIGHTED REGRESSION METHOD ON OPEN UNEMPLOYMENT RATE DATA IN INDONESIA</dc:title>
	<dc:creator>Saputri, Marisa Nanda</dc:creator>
	<dc:creator>Sifriyani, Sifriyani</dc:creator>
	<dc:creator>Wasono, Wasono</dc:creator>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-12-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9460</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss4pp2071-2080</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications; 2071-2080</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss4year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9460/7193</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Marisa Nanda Saputri, Sifriyani Sifriyani, Wasono Wasono</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>