Melola Discovery

← Melola Discovery

Jurnal

Metode Geographically Weighted Lasso dalam Pemodelan Tingkat Pengangguran Terbuka di Sulawesi Selatan

ESTIMASI: Journal of Statistics and Its Application · Vol. 6 No. 1 (2025) · pp. 70–78

DOI
10.20956/ejsa.v6i1.30863
ISSN
2721-379X, 2721-3803
Published
2025-02-17
Publisher
Hasanuddin University
Language
en

Abstract

The Open Unemployment Rate (TPT) in South Sulawesi which reached 6.07% in 2020 has an impact on the economy and welfare levels. TPT data in South Sulawesi has spatial diversity. To overcome spatial diversity in data analysis, the Geographically Weighted Regression (GWR) method can be used. However, GWR is less than optimal if multicollinearity occurs, so the Geographically Weighted Lasso (GWL) method is more appropriate. Research related to GWL on TPT in South Sulawesi has not been conducted. This study aims to obtain a GWL model with a spatial weighting matrix using a fixed exponential kernel weighting function and identify factors that influence TPT. The data used are TPT, population growth rate, literacy rate, illiteracy rate, average length of schooling, job vacancies, and job seekers. The results of the study showed that the factors influencing TPT were population growth rate, illiteracy rate, average length of schooling, and job vacancies in several districts/cities with an R2 value of 89.4%.

Keywords

Collaboration network of the authors

7 authors · 13 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mutmainnah MutmainnahAffiliation unknown1
Siti Choirotun Aisyah PutriAffiliation unknown1
Afifah SalsabilaAffiliation unknown1
Shafira SuardiAffiliation 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://journal.unhas.ac.id/index.php/ESTIMASI/oai, first harvested 2 Nov 2026, last seen 2 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">Metode Geographically Weighted Lasso dalam Pemodelan Tingkat Pengangguran Terbuka di Sulawesi Selatan</dc:title>
	<dc:creator xml:lang="en">Isnaini, Wulan Maulia</dc:creator>
	<dc:creator xml:lang="en">Aswi, Aswi</dc:creator>
	<dc:creator xml:lang="en">Sudarmin, Sudarmin</dc:creator>
	<dc:subject xml:lang="en">Spatial Heterogeneity</dc:subject>
	<dc:subject xml:lang="en">Geographically Weighted Regression (GWR)</dc:subject>
	<dc:subject xml:lang="en">Multicollinearity</dc:subject>
	<dc:subject xml:lang="en">Geographically Weighted Lasso (GWL)</dc:subject>
	<dc:subject xml:lang="en">Open Unemployment Rate</dc:subject>
	<dc:description xml:lang="en">The Open Unemployment Rate (TPT) in South Sulawesi which reached 6.07% in 2020 has an impact on the economy and welfare levels. TPT data in South Sulawesi has spatial diversity. To overcome spatial diversity in data analysis, the Geographically Weighted Regression (GWR) method can be used. However, GWR is less than optimal if multicollinearity occurs, so the Geographically Weighted Lasso (GWL) method is more appropriate. Research related to GWL on TPT in South Sulawesi has not been conducted. This study aims to obtain a GWL model with a spatial weighting matrix using a fixed exponential kernel weighting function and identify factors that influence TPT. The data used are TPT, population growth rate, literacy rate, illiteracy rate, average length of schooling, job vacancies, and job seekers. The results of the study showed that the factors influencing TPT were population growth rate, illiteracy rate, average length of schooling, and job vacancies in several districts/cities with an R2 value of 89.4%.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2025-02-17</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://journal.unhas.ac.id/index.php/ESTIMASI/article/view/30863</dc:identifier>
	<dc:identifier>10.20956/ejsa.v6i1.30863</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 6, No. 1, Januari, 2025 : Estimasi; 70-78</dc:source>
	<dc:source>2721-379X</dc:source>
	<dc:source>2721-3803</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/ESTIMASI/article/view/30863/12552</dc:relation>
</oai_dc:dc>