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Pemodelan Semiparametrik Geographical Weighted Logistic Regression pada Data Kemiskinan di Provinsi Sulawesi Selatan Tahun 2017

ESTIMASI: Journal of Statistics and Its Application · Vol. 2 No. 2 (2021) · pp. 105–114

DOI
10.20956/ejsa.v2i2.11309
ISSN
2721-379X, 2721-3803
Published
2021-07-23
Publisher
Hasanuddin University
Language
en

Abstract

The level of poverty in a Regency/city in South Sulawesi in 2017 is different. The grouping of poverty status can be done based on the value of the HeadCount Index (HCI) of South Sulawesi. Factors affecting poverty will differ for each area being observed. The statistical modeling method developed for data analysis by taking into account the location factor is semiparametric Geographical Weighted Logistic Regression (GWLR). The GWLR semiparametric Model consists of parameters that are affected by the location and not affected by the location. The parameter estimator of the GWLR semiparametric model used in this research was obtained using the maximum method likelihood estimation. The result of a semiparametric model of GWLR each district/city in South Sulawesi in 2017 has the value Estimator parameter for global parameters is the same value for each location, namely, a3 = 0.1724, a4 = 0.0204, and a6 = 0.0261 whereas the parameter estimator for local parameters has different values so that GWLR semiparametric model of each district/city.

Keywords

Collaboration network of the authors

39 authors · 97 co-authorship links

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  • Hasanuddin University
  • Affiliation unknown
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  • Same organisation
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Co-authorOrganisationJoint articles
Anisa AnisaHasanuddin University3
Sitti SahrimanAffiliation unknown3
Nirwan IlyasHasanuddin University2
Raupong RaupongHasanuddin University2
Ainul FajriAffiliation unknown2
R KhaeratiHasanuddin University1
Dwicahyo Ramadhan PriyatnaHasanuddin University1
Grace Oktavia YusufAffiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Georgina Maria TinungkiHasanuddin University1
Andi Isna YunitaHasanuddin University1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Alimatun NajihaHasanuddin University1
Anisa AnisaAffiliation unknown1
Nasrah SirajangHasanuddin University1
Reski AmalahAffiliation unknown1
NUR FUADIL MAQNUNAffiliation unknown1
Nurtit SunusiAffiliation unknown1
Sitti SahrimanHasanuddin University1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Nurtiti SunusiHasanuddin University1
Ika Pratiwi HayaAffiliation unknown1
Anna IslamiyatiHasanuddin University1
Andi Ummi Melin AichaAffiliation unknown1
Wa Ode Sitti Amni AmniAffiliation unknown1
Amalia AndrianingrumAffiliation unknown1
Erna Tri HerdianiHasanuddin University1
Fadhila Febriyanti NajamuddinHasanuddin University1
Nur ErawatiAffiliation unknown1
Sunarti SunartiAffiliation unknown1
Niswar AliansaAffiliation unknown1
Sutrianah BurhanAffiliation unknown1
Siswanto SiswantoHasanuddin University1
Arief Rahman NurHasanuddin University1
Andi Muhammad HakamHasanuddin University1

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 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">Pemodelan Semiparametrik Geographical Weighted Logistic Regression pada Data Kemiskinan di Provinsi Sulawesi Selatan Tahun 2017</dc:title>
	<dc:creator xml:lang="en">Fitriatusakiah, Fitriatusakiah</dc:creator>
	<dc:creator xml:lang="en">Jaya, Andi Kresna</dc:creator>
	<dc:creator xml:lang="en">Talangko, La Podje</dc:creator>
	<dc:subject xml:lang="en">Proverty</dc:subject>
	<dc:subject xml:lang="en">HCI</dc:subject>
	<dc:subject xml:lang="en">GWLR Semiparametric</dc:subject>
	<dc:subject xml:lang="en">Maximum Likelihood Estimation</dc:subject>
	<dc:description xml:lang="en">The level of poverty in a Regency/city in South Sulawesi in 2017 is different. The grouping of poverty status can be done based on the value of the HeadCount Index (HCI) of South Sulawesi. Factors affecting poverty will differ for each area being observed. The statistical modeling method developed for data analysis by taking into account the location factor is semiparametric Geographical Weighted Logistic Regression (GWLR). The GWLR semiparametric Model consists of parameters that are affected by the location and not affected by the location. The parameter estimator of the GWLR semiparametric model used in this research was obtained using the maximum method likelihood estimation. The result of a semiparametric model of GWLR each district/city in South Sulawesi in 2017 has the value Estimator parameter for global parameters is the same value for each location, namely, a3 = 0.1724, a4 = 0.0204, and a6 = 0.0261 whereas the parameter estimator for local parameters has different values so that GWLR semiparametric model of each district/city. </dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2021-07-23</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/11309</dc:identifier>
	<dc:identifier>10.20956/ejsa.v2i2.11309</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 2, No. 2, Juli, 2021 : Estimasi; 105-114</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/11309/7099</dc:relation>
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