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Perbandingan Model Threshold Generalized utoregressive Conditional Heteroscedasticity dan Exponential Generalized Autoregressive Conditional eteroscedasticity pada Peramalan Curah Hujan

ESTIMASI: Journal of Statistics and Its Application · Vol. 6 No. 2 (2025) · pp. 196–212

DOI
10.20956/ejsa.v6i2.43100
ISSN
2721-379X, 2721-3803
Published
2025-08-04
Publisher
Hasanuddin University
Language
en

Abstract

Rainfall plays an important role in life and is closely related to other weather elements. Rainfall data is used for various purposes, including flood and drought risk mitigation and water resource planning. Makassar City has significant rainfall variability and requires accurate forecasting to manage its negative impacts. This study aims to predict rainfall in Makassar City from January 2021 to May 2023. The methods used are Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The results showed that the ARMA (2,1)-GARCH (1,2) model had MAPE and RMSEP values ​​of 1.234 and 33.411, respectively. The ARMA (2,1)-TGARCH (2,1) model had MAPE and RMSEP values ​​of 1.330 and 29.357, respectively. The ARMA (2,1)-EGARCH (1,2) model has MAPE and RMSEP values ​​of 0.924 and 32.641, respectively. The smallest MAPE and RMSEP values ​​are in the ARMA (2,1)-EGARCH (1,2) model. Thus, the ARMA (2,1)-EGARCH (1,2) model was selected as the best or optimal model for rainfall forecasting in Makassar City.

Keywords

Collaboration network of the authors

40 authors · 89 co-authorship links · showing the 40 most connected

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  • Sultan Ageng Tirtayasa University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
La Podje TalangkoAffiliation unknown4
Nirwan IlyasAffiliation unknown2
Erna Tri HerdianiAffiliation unknown2
Raupong RaupongAffiliation unknown2
Georgina Maria TinungkiAffiliation unknown2
Andi Isna YunitaAffiliation unknown2
Nasrah SirajangAffiliation unknown2
Anisa Haura Salsa Fatih YusufAffiliation unknown2
Rusydah KhaeratiAffiliation unknown1
Grace Oktavia YusufAffiliation unknown1
Rizki AdiputraAffiliation unknown1
Risma RismaAffiliation unknown1
Dewi Santika Upa P.Affiliation unknown1
Fitriatusakiah FitriatusakiahAffiliation unknown1
Andi Tenri Riski AmaliaAffiliation unknown1
Ririn AriantiAffiliation unknown1
Muhammad IdmanAffiliation unknown1
Anisa AnisaSultan Ageng Tirtayasa University1
Najiha AlimatunAffiliation unknown1
Reski AmalahAffiliation unknown1
Fitri AnnisaAffiliation unknown1
Nur Fuadil Maqnun WahabAffiliation unknown1
Nurtit SunusiAffiliation unknown1
Mukrimin AdamAffiliation unknown1
Ainul FajriAffiliation unknown1
Nurtiti SunusiAffiliation unknown1
Ika Pratiwi HayaAffiliation unknown1
Anna IslamiyatiAffiliation unknown1
Andi Ummi Melin Aicha AichaAffiliation unknown1
Hendriete Tiur Marowi WaibusiAffiliation unknown1
Rael Hofni TandirerungAffiliation unknown1
Wa Ode Sitti Amni AmniAffiliation unknown1
Nirwan NirwanAffiliation unknown1
Nurhidayatullah NurhidayatullahAffiliation unknown1
Rini JamalAffiliation unknown1
Andi M Alfin BasoAffiliation unknown1
Andi FebriyantiAffiliation 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">Perbandingan Model Threshold Generalized utoregressive Conditional Heteroscedasticity dan Exponential Generalized Autoregressive Conditional eteroscedasticity pada Peramalan Curah Hujan</dc:title>
	<dc:creator xml:lang="en">Andrianingrum, Amalia</dc:creator>
	<dc:creator xml:lang="en">Sahriman, Sitti</dc:creator>
	<dc:creator xml:lang="en">Jaya, Andi Kresna</dc:creator>
	<dc:subject xml:lang="en">ARMA</dc:subject>
	<dc:subject xml:lang="en">Threshold Generalized Autoregressive Conditional Heteroscedasticity</dc:subject>
	<dc:subject xml:lang="en">Exponential Generalized Autoregressive Conditional Heteroscedasticity</dc:subject>
	<dc:subject xml:lang="en">Rainfall</dc:subject>
	<dc:subject xml:lang="en">MAPE</dc:subject>
	<dc:subject xml:lang="en">RMSEP</dc:subject>
	<dc:description xml:lang="en">Rainfall plays an important role in life and is closely related to other weather elements. Rainfall data is used for various purposes, including flood and drought risk mitigation and water resource planning. Makassar City has significant rainfall variability and requires accurate forecasting to manage its negative impacts. This study aims to predict rainfall in Makassar City from January 2021 to May 2023. The methods used are Threshold Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) and Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH). The results showed that the ARMA (2,1)-GARCH (1,2) model had MAPE and RMSEP values ​​of 1.234 and 33.411, respectively. The ARMA (2,1)-TGARCH (2,1) model had MAPE and RMSEP values ​​of 1.330 and 29.357, respectively. The ARMA (2,1)-EGARCH (1,2) model has MAPE and RMSEP values ​​of 0.924 and 32.641, respectively. The smallest MAPE and RMSEP values ​​are in the ARMA (2,1)-EGARCH (1,2) model. Thus, the ARMA (2,1)-EGARCH (1,2) model was selected as the best or optimal model for rainfall forecasting in Makassar City.</dc:description>
	<dc:publisher xml:lang="en">Hasanuddin University</dc:publisher>
	<dc:date>2025-08-04</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/43100</dc:identifier>
	<dc:identifier>10.20956/ejsa.v6i2.43100</dc:identifier>
	<dc:source xml:lang="en">ESTIMASI: Journal of Statistics and Its Application; Vol. 6, No. 2, Juli, 2025 : Estimasi; 196-212</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/43100/13222</dc:relation>
</oai_dc:dc>