Melola Discovery

← Melola Discovery

Journal

KONSTRUKSI UJI KESESUAIAN MODEL GEOGRAPHICALLY WEIGHTED POLYNOMIAL REGRESSION

Jurnal Matematika, Statistika dan Komputasi · Vol. 15 No. 2 (2018) · pp. 26–33

DOI
10.20956/jmsk.v15i2.5711
ISSN
2614-8811, 1858-1382
Published
2018-12-20
Publisher
Department of Mathematics, Hasanuddin University
Language
en

Abstract

AbstractGeographically Weighted Polynomial Regression (GWPolR) is a generalization of Geographically Weighted Regression (GWR) model. By using the generalization, GWPolR has parameters much more than GWR model. In general, excess of the number of parameter will have a higher appropriate value. However, the model which has less parameter will have the excess for easing in application and its interpretation. Nevertheless, when the model has more the parameters, then the model will be better significantly to be used. Therefore, the aim of this paper is to construct the conformity between hypothesis test with respect to the GWPolR model. Keywords: Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model AbstrakGeographically Weighted Polynomial Regression (GWPolR) merupakan perumuman dari model Geographically Weighted Regression (GWR). Dengan perumuman tersebut, model GWPolR memiliki jumlah parameter yang lebih banyak daripada model GWR. Umumnya, kelebihan model dengan jumlah parameter lebih banyak adalah memiliki nilai kesesuaian lebih tinggi. Sebaliknya, model dengan jumlah parameter yang sedikit memiliki kelebihan berupa kemudahan dalam aplikasi dan interpretasinya. Namun demikian, jika model dengan jumlah parameter yang lebih banyak ternyata secara signifikan lebih baik maka sudah seharusnya model tersebut dipilih untuk digunakan. Oleh karena itu, tujuan paper ini adalah mengkonstruksi uji hipotesis kesesuaian model GWPolR. Kata Kunci: Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model

Keywords

Collaboration network of the authors

40 authors · 112 co-authorship links · 3 across different organisations · showing the 40 most connected

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

  • Universitas Airlangga
  • Universitas Wijaya Kusuma Surabaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Toha SaifudinUniversitas Airlangga7
Juan Krisfigo PrasetyoAffiliation unknown2
Berliani LarasatiAffiliation unknown2
Bagas Shata PratamaAffiliation unknown2
Dita AmeliaAffiliation unknown2
Rien RecyliaAffiliation unknown2
Belindha Ayu ArdhaniUniversitas Airlangga1
Nia SaurinaUniversitas Wijaya Kusuma Surabaya1
Riries RulaningtyasUniversitas Airlangga1
Aryati AryatiUniversitas Airlangga1
Rizky Ismaul Uyun HidayatAffiliation unknown1
Mutiara AisharezkaAffiliation unknown1
Naufal Ramadhan Al Akhwal SiregarAffiliation unknown1
Muhammad Fikry Al FariziAffiliation unknown1
Atikah FaizaAffiliation unknown1
Muhammad Hilmi FibryanAffiliation unknown1
Alda Fuadiyah SuryonoAffiliation unknown1
Nina AuliyahAffiliation unknown1
Alfi Nur NitasariAffiliation unknown1
Andini Sa'idahAffiliation unknown1
Nurin FaizunAffiliation unknown1
Kezia Eunike DarmawanAffiliation unknown1
Marfa Audilla FitriAffiliation unknown1
Muhammad Althof JuniarAffiliation unknown1
Azzahra FaniaAffiliation unknown1
Diana UlyaAffiliation unknown1
Rico RamadhanAffiliation unknown1
Nadhira Safa KamiilahAffiliation unknown1
Putu Eka AndrianiAffiliation unknown1
Cynthia Anggelyn SiburianAffiliation unknown1
Muhammad Rizaldy BaihaqiAffiliation unknown1
Salma Bethari Andjani SAffiliation unknown1
Alma Khalisa RahmaAffiliation unknown1
Qumadha Zaenal AbidinAffiliation unknown1
Steven SoewignjoAffiliation unknown1
Ni Wayan Widya Septia SariAffiliation unknown1
Andini Putri MedianiAffiliation unknown1
M. Aqil Zaidan KamilAffiliation unknown1
Ardana Tegar HendrawanAffiliation 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/jmsk/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">KONSTRUKSI UJI KESESUAIAN MODEL GEOGRAPHICALLY WEIGHTED POLYNOMIAL REGRESSION</dc:title>
	<dc:creator xml:lang="en">Chamidah, Nur</dc:creator>
	<dc:subject xml:lang="en">Geographically weighted polynomial regression</dc:subject>
	<dc:subject xml:lang="en">Geographically Weighted Regression</dc:subject>
	<dc:subject xml:lang="en">uji kesesuaian model</dc:subject>
	<dc:description xml:lang="en">AbstractGeographically Weighted Polynomial Regression (GWPolR) is a generalization of   Geographically Weighted Regression (GWR) model. By using the generalization, GWPolR has parameters much more than GWR model. In general, excess of the number of parameter will have a higher appropriate value. However, the model which has less parameter will have the excess for easing in application and its interpretation. Nevertheless, when the model has more the parameters, then the model will be better significantly to be used.  Therefore, the aim of this paper is to construct the conformity between hypothesis test with respect to the GWPolR model. Keywords: Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model AbstrakGeographically Weighted Polynomial Regression (GWPolR) merupakan perumuman dari model Geographically Weighted Regression (GWR). Dengan perumuman tersebut, model GWPolR memiliki jumlah parameter yang lebih banyak daripada model GWR. Umumnya, kelebihan model dengan jumlah parameter lebih banyak adalah memiliki nilai kesesuaian lebih tinggi. Sebaliknya, model dengan jumlah parameter yang sedikit memiliki kelebihan berupa kemudahan dalam aplikasi dan interpretasinya. Namun demikian, jika model dengan jumlah parameter yang lebih banyak ternyata secara signifikan lebih baik maka sudah seharusnya model tersebut dipilih untuk digunakan. Oleh karena itu, tujuan paper ini adalah mengkonstruksi uji hipotesis kesesuaian model GWPolR. Kata Kunci:    Geographically weighted polynomial regression, Geographically Weighted Regression, uji kesesuaian model</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2018-12-20</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/jmsk/article/view/5711</dc:identifier>
	<dc:identifier>10.20956/jmsk.v15i2.5711</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 15 No. 2 (2019): JMSK Vol. 15, No. 2, January 2019; 26-33</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/5711/3148</dc:relation>
	<dc:relation>10.20956/j.v15i2.5711.g3148</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2018 Jurnal Matematika Statistika dan Komputasi</dc:rights>
</oai_dc:dc>