Melola Discovery

← Melola Discovery

Jurnal

Pemodelan Geographically Weighted Logistic Regression dengan Metode Ridge

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 2 (2023) · pp. 130–143

DOI
10.20956/ejsa.v4i2.12250
ISSN
2721-379X, 2721-3803
Published
2023-08-04
Publisher
Hasanuddin University
Language
en

Abstract

One of the goals of national development is to reduce poverty. Poverty is included in the phenomenon of spatial heterogeneity because it can be shown by the varying economic conditions in each region. The statistical modeling method developed for data analysis takes into account regional factors namely Geographical Weighted Logistic Regression (GWLR). The parameter estimator of the GWLR semiparametric model used in this study was obtained using the Maximum Likelihood Estimation method. In GWLR, the assumption that must be fulfilled is the absence of multicollinearity. One method for dealing with multicollinearity is ridge regression involving the addition of a constant bias. The results obtained were the MSE value of the parameter estimator with the ridge method (707.77) smaller than the GWLR model before using the ridge (715.88). This shows that the ridge method is more effective if there are multicollinearity problems.

Keywords

Collaboration network of the authors

35 authors · 79 co-authorship links

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

  • Sultan Ageng Tirtayasa University
  • Institut Agama Islam Negeri Kendari
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Georgina Maria TinungkiAffiliation unknown3
Sitti SahrimanAffiliation unknown3
Nirwan IlyasAffiliation unknown2
La Podje TalangkoAffiliation unknown2
Erna Tri HerdianiAffiliation unknown2
Raupong RaupongAffiliation unknown2
Rusydah KhaeratiAffiliation unknown1
Grace Oktavia YusufAffiliation unknown1
Iis Cendrah KasihAffiliation unknown1
Fitriatusakiah FitriatusakiahAffiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Anisa AnisaSultan Ageng Tirtayasa University1
Najiha AlimatunAffiliation unknown1
Ratmila MammiAffiliation unknown1
Nur Alfianingsih RajaAffiliation unknown1
Nur Fuadil Maqnun WahabAffiliation unknown1
Nurtit SunusiAffiliation unknown1
Anisa KalondengAffiliation unknown1
Nurkamalia NurkamaliaAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Ainul FajriAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Ika Pratiwi HayaAffiliation unknown1
Anna IslamiyatiAffiliation unknown1
Andi Ummi Melin Aicha AichaAffiliation unknown1
Wa Ode Sitti Amni AmniAffiliation unknown1
Nuralyatussa’ adaAffiliation unknown1
Nurul HikmahInstitut Agama Islam Negeri Kendari1
Parida Ayu NingsiAffiliation unknown1
Amalia AndrianingrumAffiliation 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">Pemodelan Geographically Weighted Logistic Regression dengan Metode Ridge</dc:title>
	<dc:creator xml:lang="en">Amalah, Reski</dc:creator>
	<dc:creator xml:lang="en">Jaya, Andi Kresna</dc:creator>
	<dc:creator xml:lang="en">Sirajang, Nasrah</dc:creator>
	<dc:subject xml:lang="en">Proverty</dc:subject>
	<dc:subject xml:lang="en">GWLR</dc:subject>
	<dc:subject xml:lang="en">Maximum Likelihood Estimation</dc:subject>
	<dc:subject xml:lang="en">multikolinieritas</dc:subject>
	<dc:subject xml:lang="en">ridge</dc:subject>
	<dc:description xml:lang="en">One of the goals of national development is to reduce poverty. Poverty is included in the phenomenon of spatial heterogeneity because it can be shown by the varying economic conditions in each region. The statistical modeling method developed for data analysis takes into account regional factors namely Geographical Weighted Logistic Regression (GWLR). The parameter estimator of the GWLR semiparametric model used in this study was obtained using the Maximum Likelihood Estimation method. In GWLR, the assumption that must be fulfilled is the absence of multicollinearity. One method for dealing with multicollinearity is ridge regression involving the addition of a constant bias . The results obtained were the MSE value of the parameter estimator with the ridge method (707.77) smaller than the GWLR model before using the ridge (715.88). This shows that the ridge method is more effective if there are multicollinearity problems.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2023-08-04</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/12250</dc:identifier>
	<dc:identifier>10.20956/ejsa.v4i2.12250</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 2, Juli, 2023 : Estimasi; 130-143</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/12250/10204</dc:relation>
</oai_dc:dc>