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Pemodelan Statistical Downscaling dengan Regresi Modifikasi Jackknife Ridge Dummy Berbasis K-means untuk Pendugaan Curah Hujan

ESTIMASI: Journal of Statistics and Its Application · Vol. 2 No. 1 (2021) · pp. 19–28

DOI
10.20956/ejsa.v2i1.11189
ISSN
2721-379X, 2721-3803
Published
2021-05-15
Publisher
Hasanuddin University
Language
en

Abstract

Indonesia is a tropical country, which only has two seasons throughout the year, namely the dry season and the rainy season. Thus, it is likely that rain will continue to fall during the dry season, which has a serious impact on various sectors of life. General Circulation Model (GCM) is used to deal with climate change, but the GCM cannot conduct simulations well for local scale climate variables. Therefore, Statistical Downscaling (SD) is used to predict local scale rainfall in the district of Pangkep based on square GCM (CMIP5) 8 × 8 grid data. Modified jackknife ridge regression is used to overcome multicollinearity problems that occur in GCM-lag data. Three dummy variables were added as predictor variables for the model to overcome the heterogeneity of the various forms. SD model MJR dummy regression gives good results based on the coefficient of determination and high correlation with lower root mean square error and root mean square error prediction.

Collaboration network of the authors

28 authors · 78 co-authorship links

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Co-authorOrganisationJoint articles
Erna Tri HerdianiAffiliation unknown2
La Podje TalangkoAffiliation unknown2
Andi Kresna JayaAffiliation unknown2
Rizki AdiputraAffiliation unknown1
Risma RismaAffiliation unknown1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Raupong RaupongAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Nasrah SirajangAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Anisa Haura Salsa Fatih YusufAffiliation unknown1
Georgina Maria TinungkiAffiliation unknown1
Hendriete Tiur Marowi WaibusiAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Nirwan NirwanAffiliation unknown1
Nurhidayatullah NurhidayatullahAffiliation unknown1
Amalia AndrianingrumAffiliation unknown1
Siswanto SiswantoAffiliation unknown1
Andi Isna YunitaAffiliation unknown1
Muhammad FadilAffiliation unknown1
A.Muthiah Nur AngrianyAffiliation unknown1
Rini JamalAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
Andi FebriyantiAffiliation unknown1
Rahmiati RahimAffiliation 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">Pemodelan Statistical Downscaling dengan Regresi Modifikasi Jackknife Ridge Dummy Berbasis K-means untuk Pendugaan Curah Hujan</dc:title>
	<dc:creator xml:lang="en">P., Dewi Santika Upa</dc:creator>
	<dc:creator xml:lang="en">Sahriman, Sitti</dc:creator>
	<dc:description xml:lang="en">Indonesia is a tropical country, which only has two seasons throughout the year, namely the dry season and the rainy season. Thus, it is likely that rain will continue to fall during the dry season, which has a serious impact on various sectors of life. General Circulation Model (GCM) is used to deal with climate change, but the GCM cannot conduct simulations well for local scale climate variables. Therefore, Statistical Downscaling (SD) is used to predict local scale rainfall in the district of Pangkep based on square GCM (CMIP5) 8 × 8 grid data. Modified jackknife ridge regression is used to overcome multicollinearity problems that occur in GCM-lag data. Three dummy variables were added as predictor variables for the model to overcome the heterogeneity of the various forms. SD model MJR dummy regression gives good results based on the coefficient of determination and high correlation with lower root mean square error and root mean square error prediction.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2021-05-15</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/11189</dc:identifier>
	<dc:identifier>10.20956/ejsa.v2i1.11189</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 2, No. 1, Januari, 2021 : Estimasi; 19-28</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/11189/6791</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2021 ESTIMASI: Journal of Statistics and Its Application</dc:rights>
</oai_dc:dc>