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IMPLEMENTATION OF THE STEP FUNCTION INTERVENTION AND EXTREME LEARNING MACHINE FOR FORECASTING THE PASSENGER’S AIRPORT IN SORONG

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 1 (2023) · pp. 0535–0544

DOI
10.30598/barekengvol17iss1pp0535-0544
ISSN
2615-3017, 1978-7227
Published
2023-04-20
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This study aims to forecast the number of passengers departing at the domestic departure terminal at Domine Eduard Osok Sorong Airport in 2022 using the Autoregressive Integrated Moving Average (ARIMA) method, ARIMA with Step Function Intervention, and Extreme Learning Machine (ELM). The knowledge of the number of passengers can help the airport prepare facilities. The residual ARIMA model (0,1,0) has no serial correlation (random walk) based on the Ljung-Box test. The MAPE value of the ARIMA model (0,1,0) is 65.47% which means poorly fitted. Because of it, the researchers propose an intervention in the ARIMA model. The RMSE and MAPE ARIMA Intervention ​​(1,0,0) (0,1,0) [12] were 9,027.671 and 35.86%, respectively. Besides, this study also employed the ELM method, which has a MAPE error measurement value of 30.64%. The ELM method has the lowest error measurement results among the three methods. Therefore, the ELM method is suitable for forecasting the number of passengers with predicted values ​​from June to September 2022 as follows: 47985, 37821, 31247, and 33578. On the other hand, intervention in ARIMA can reduce MAPE by 45%.

Collaboration network of the authors

22 authors · 35 co-authorship links

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  • Islamic University of Indonesia
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nahdah Ghina HandayaniIslamic University of Indonesia1
Rinaldi SjahrilAffiliation unknown1
A. Sri Sartika Sufiina AhmadAffiliation unknown1
Herlinda YanasariAffiliation unknown1
Muh. Yunus SuriaAffiliation unknown1
Nur Qadri FebriantiAffiliation unknown1
Muhammad BariklanaAffiliation unknown1
Octavia RahmawatiAffiliation unknown1
Nanda Lailatul HumairahAffiliation unknown1
Ashabul Akbar MaulanaAffiliation unknown1
Alfa YulianaAffiliation unknown1
Fitri AmaliaAffiliation unknown1
Nabila Dwi FaziraAffiliation unknown1
IKA MEIZA MAHARANIAffiliation unknown1
Mega Luna SuliztiaAffiliation unknown1
Malecita Nur Atala SinggihAffiliation unknown1
Shazia Ayesha AzzahraAffiliation unknown1
Wiranti Nugrah AndiniAffiliation unknown1
Irwan SutisnaAffiliation 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) 
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	<dc:title xml:lang="en-US">IMPLEMENTATION OF THE STEP FUNCTION INTERVENTION AND EXTREME LEARNING MACHINE FOR FORECASTING THE PASSENGER’S AIRPORT IN SORONG</dc:title>
	<dc:creator>Faizin, Nur</dc:creator>
	<dc:creator>Fauzan, Achmad</dc:creator>
	<dc:creator>Primandari, Arum Handini</dc:creator>
	<dc:description xml:lang="en-US">This study aims to forecast the number of passengers departing at the domestic departure terminal at Domine Eduard Osok Sorong Airport in 2022 using the Autoregressive Integrated Moving Average (ARIMA) method, ARIMA with Step Function Intervention, and Extreme Learning Machine (ELM). The knowledge of the number of passengers can help the airport prepare facilities. The residual ARIMA model (0,1,0) has no serial correlation (random walk) based on the Ljung-Box test. The MAPE value of the ARIMA model (0,1,0) is 65.47% which means poorly fitted. Because of it, the researchers propose an intervention in the ARIMA model. The RMSE and MAPE ARIMA Intervention ​​(1,0,0) (0,1,0) [12] were 9,027.671 and 35.86%, respectively. Besides, this study also employed the ELM method, which has a MAPE error measurement value of 30.64%. The ELM method has the lowest error measurement results among the three methods. Therefore, the ELM method is suitable for forecasting the number of passengers with predicted values ​​from June to September 2022 as follows: 47985, 37821, 31247, and 33578. On the other hand, intervention in ARIMA can reduce MAPE by 45%.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-04-20</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7686</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss1pp0535-0544</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications; 0535-0544</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss1year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/7686/5572</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Nur Faizin, NF, Achmad Fauzan, AF, Arum Handini Primandari, AHP</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>