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Performa Model Statistical Downscaling dengan Peubah Dummy Berdasarkan K-Means dan Average Linkage

ESTIMASI: Journal of Statistics and Its Application · Vol. 4 No. 2 (2023) · pp. 165–175

DOI
10.20956/ejsa.v4i2.12658
ISSN
2721-379X, 2721-3803
Published
2023-08-04
Publisher
Hasanuddin University
Language
en

Abstract

Climate change that occurs is often used to predict future climate conditions. For future climate predictions it is only possible to use climate models. One of the climate models used to predict climate conditions is the Global Circulation Models (GCM). GCM represents global climatic conditions but not on a regional or local scale. The approach that has been widely used to bridge the difference in scale is statistical downscaling. Large-scale GCM data allows for multicollinearity. estimation liu regression and principal component regression is used to solve the multicollinearity problem. In addition, dummy variables based on k-means and average linkage are used in the model to overcome the heterogeneous variance of residue. There are 4 dummy variables in the cluster technique. In this paper, Liu k-means regression model parameter estimation method is the best model.

Keywords

Collaboration network of the authors

40 authors · 116 co-authorship links · showing the 40 most connected

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
Anisa AnisaSultan Ageng Tirtayasa University7
Anna IslamiyatiAffiliation unknown5
A.Muthiah Nur AngrianyAffiliation unknown5
Siswanto SiswantoAffiliation unknown4
Nirwan IlyasAffiliation unknown3
La Podje TalangkoAffiliation unknown3
Andi Kresna JayaAffiliation unknown3
Erna Tri HerdianiAffiliation unknown2
Muh. Ikbal TaufikAffiliation unknown2
Anisa KalondengAffiliation unknown2
M. Zaky Hisyam GozhiAffiliation unknown2
Muhammad FadilAffiliation unknown2
Nasrah SirajangAffiliation unknown2
Georgina Maria TinungkiAffiliation unknown2
Zhazha Alifkhamulki RamdhaniAffiliation unknown1
Samsul ArifinAffiliation unknown1
Andi Sitti Fahmi Riyanti HufainiAffiliation unknown1
Dwicahyo Ramadhan PriyatnaAffiliation unknown1
Vieri KoerniawanAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Dewi Rahma EnteAffiliation unknown1
Rizki AdiputraAffiliation unknown1
Risma RismaAffiliation unknown1
Dewi Santika Upa P.Affiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Hendriete Tiur Marowi WaibusiAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Asnidar AsnidarAffiliation unknown1
Nirwan NirwanAffiliation unknown1
Nurhidayatullah NurhidayatullahAffiliation unknown1
Nurul HikmahInstitut Agama Islam Negeri Kendari1
Amalia AndrianingrumAffiliation unknown1
Fauziah NurhidayahAffiliation 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) 
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	<dc:title xml:lang="en">Performa Model Statistical Downscaling dengan Peubah Dummy Berdasarkan K-Means dan Average Linkage</dc:title>
	<dc:creator xml:lang="en">Fitri Annisa</dc:creator>
	<dc:creator xml:lang="en">Raupong, Raupong</dc:creator>
	<dc:creator xml:lang="en">Sahriman, Sitti</dc:creator>
	<dc:subject xml:lang="en">Average Linkage</dc:subject>
	<dc:subject xml:lang="en">Estimastion Liu Regression</dc:subject>
	<dc:subject xml:lang="en">Global Circulation Model</dc:subject>
	<dc:subject xml:lang="en">K-Means</dc:subject>
	<dc:subject xml:lang="en">Dummy Variabel</dc:subject>
	<dc:subject xml:lang="en">Principal Component Regression</dc:subject>
	<dc:subject xml:lang="en">Statistical Downscaling</dc:subject>
	<dc:description xml:lang="en">Climate change that occurs is often used to predict future climate conditions. For future climate predictions it is only possible to use climate models. One of the climate models used to predict climate conditions is the Global Circulation Models (GCM). GCM represents global climatic conditions but not on a regional or local scale. The approach that has been widely used to bridge the difference in scale is statistical downscaling. Large-scale GCM data allows for multicollinearity. estimation liu regression and principal component regression is used to solve the multicollinearity problem. In addition, dummy variables based on k-means and average linkage are used in the model to overcome the heterogeneous variance of residue. There are 4 dummy variables in the cluster technique. In this paper, Liu k-means regression model parameter estimation method is the best model.</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/12658</dc:identifier>
	<dc:identifier>10.20956/ejsa.v4i2.12658</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 4, No. 2, Juli, 2023 : Estimasi; 165-175</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/12658/10217</dc:relation>
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