Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 3

PERFORMANCE OF THE ACCURACY OF FORECASTING THE CONSUMER PRICE INDEX USING THE GARCH AND ANN METHODS

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 2 (2023) · pp. 0931–0944

DOI
10.30598/barekengvol17iss2pp0931-0944
ISSN
2615-3017, 1978-7227
Published
2023-06-11
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The Consumer Price Index (CPI) is the most widely used indicator of the inflation rate. Then, the value of CPI in the future must be known to be the basis of the government's making appropriate and accurate policies. The CPI data used in this study was taken from the Central Statistics Agency (BPS) from January 2006 - to December 2021. The CPI data used has a data pattern containing symptoms of heteroskedasticity. To overcome the symptoms of heteroskedasticity, the author uses the GARCH and ANN methods to determine the value of CPI in the future. The GARCH method can overcome the symptoms of heteroskedasticity in the time series forecasting process, while ANN is an effective method in time series forecasting because of its high level of accuracy. In this study, mape error calculation results were obtained with the ARIMA model (4,2,2)~GARCH(1.1) of 3.19% or with an accuracy of 96.81%, and ANN using two hidden layers of 1.24% or with an accuracy of 98.76%. Thus, the results of this study show that the ANN method is the best method of forecasting Consumer Price Index (CPI) data.

Collaboration network of the authors

22 authors · 46 co-authorship links

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

  • Lampung University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mustofa UsmanLampung University2
Warsono WarsonoLampung University1
Favorisen Rosyking LumbanrajaLampung University1
Nur AlifiahLampung University1
Amanto AmantoLampung University1
Warsono WarsonoLampung University1
Mudyawati KamaruddinAffiliation unknown1
Rasheed AhmadAffiliation unknown1
Rizki Agung WibowoAffiliation unknown1
Khoirin NisaAffiliation unknown1
Hilda VeneliaAffiliation unknown1
Virda KurniawatiAffiliation unknown1
Aang NuryamanAffiliation unknown1
Rizki Khoirun NisaAffiliation unknown1
Fitriani FitrianiAffiliation unknown1
Ahmad FaisolAffiliation unknown1
Rachma Allya ShieffaAffiliation unknown1
Thomas JuliansyahAffiliation 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://ojs3.unpatti.ac.id/index.php/barekeng/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-US">PERFORMANCE OF THE ACCURACY OF FORECASTING THE CONSUMER PRICE INDEX USING THE GARCH AND ANN METHODS</dc:title>
	<dc:creator>Kurniasari, Dian</dc:creator>
	<dc:creator>Mukhlisin, Zaenal</dc:creator>
	<dc:creator>Wamiliana, Wamiliana</dc:creator>
	<dc:creator>Warsono, Warsono</dc:creator>
	<dc:description xml:lang="en-US">The Consumer Price Index (CPI) is the most widely used indicator of the inflation rate. Then, the value of CPI in the future must be known to be the basis of the government's making appropriate and accurate policies. The CPI data used in this study was taken from the Central Statistics Agency (BPS) from January 2006 - to December 2021. The CPI data used has a data pattern containing symptoms of heteroskedasticity. To overcome the symptoms of heteroskedasticity, the author uses the GARCH and ANN methods to determine the value of CPI in the future. The GARCH method can overcome the symptoms of heteroskedasticity in the time series forecasting process, while ANN is an effective method in time series forecasting because of its high level of accuracy. In this study, mape error calculation results were obtained with the ARIMA model (4,2,2)~GARCH(1.1) of 3.19% or with an accuracy of 96.81%, and ANN using two hidden layers of 1.24% or with an accuracy of 98.76%. Thus, the results of this study show that the ANN method is the best method of forecasting Consumer Price Index (CPI) data.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-06-11</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8007</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss2pp0931-0944</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0931-0944</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss2year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8007/5956</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Dian Kurniasari, Zaenal Mukhlisin, Wamiliana Wamiliana, Warsono Warsono</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>