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PERFORMANCE COMPARISON OF SARIMA INTERVENTION AND PROPHET MODELS FOR FORECASTING THE NUMBER OF AIRLINE PASSENGER AT SOEKARNO-HATTA INTERNATIONAL AIRPORT

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 4 (2023) · pp. 2107–2120

DOI
10.30598/barekengvol17iss4pp2107-2120
ISSN
2615-3017, 1978-7227
Published
2023-12-19
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The impact of the COVID-19 pandemic on the air transportation sector, particularly Soekarno-Hatta (Soetta) International Airport, has been quite significant. The number of passengers at Soetta Airport has decreased due to the COVID-19 pandemic, but flight activities are still ongoing to this day. An accurate forecasting model is needed to predict the number of airline passengers at Soetta Airport with the presence of the COVID-19 pandemic as an intervention. In this study we discuss performance comparison of two models namely SARIMA intervention and Prophet in forecasting the number of domestic passengers at Soetta Airport. The research results showed that the best SARIMA intervention model was SARIMA (0,1,1)(1,0,0)12 b = 0, s = 20, r = 0, with a Mean Absolute Percentage Error (MAPE) of 28% and Root Mean Square Error (RMSE) of 433473. On the other hand, the Prophet model yielded a MAPE of 37% and an RMSE of 497154. In terms of MAPE and RMSE, the SARIMA intervention method provides better results than the Prophet model in forecasting the number of domestic passengers at Soetta Airport.

Collaboration network of the authors

40 authors · 199 co-authorship links · 5 across different organisations · showing the 40 most connected

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  • IPB University
  • Bandung Islamic University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Laily Nissa Atul MualifahAffiliation unknown14
Bagus SartonoIPB University10
Anang KurniaAffiliation unknown3
Erfiani ErfianiAffiliation unknown2
Teguh PrasetyoIPB University2
Rizki Alifah PutriIPB University2
Dini RamadhaniIPB University2
Dwi Agustin Nuriani SirodjAffiliation unknown1
Dhiya Khalishah Tsany SuwarsoAffiliation unknown1
Akbar RizkiAffiliation unknown1
Salsabila Dwi RahmiAffiliation unknown1
Hakim Zoelva MahesaAffiliation unknown1
Windi GunawanAffiliation unknown1
Zafira Ilma FitriAffiliation unknown1
Adelia PutriAffiliation unknown1
Muhammad Rizky NurhambaliAffiliation unknown1
Dhiya Khalishah Tsany SuwarsoAffiliation unknown1
Akbar RizkiAffiliation unknown1
Salsabila Dwi RahmiAffiliation unknown1
Hakim Zoelva MahesaAffiliation unknown1
Windi GunawanAffiliation unknown1
Zafira Ilma FitriAffiliation unknown1
Adelia PutriAffiliation unknown1
Muhammad Rizky NurhambaliAffiliation unknown1
Vera Maya SantiAffiliation unknown1
Indahwati IndahwatiAffiliation unknown1
Hafizah IlmaAffiliation unknown1
Budi SusetyoAffiliation unknown1
Andika Putri RatnasariAffiliation unknown1
Kusman SadikAffiliation unknown1
Rahmatin Nur AmaliaAffiliation unknown1
Asrirawan AsrirawanAffiliation unknown1
Muhammad Azis SuprayogiAffiliation unknown1
Agus Sofian Eka HidayatAffiliation unknown1
Dian KusumaningrumAffiliation 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">PERFORMANCE COMPARISON OF SARIMA INTERVENTION AND PROPHET MODELS FOR FORECASTING THE NUMBER OF AIRLINE PASSENGER AT SOEKARNO-HATTA INTERNATIONAL AIRPORT</dc:title>
	<dc:creator>Nur Aziza, Vivin</dc:creator>
	<dc:creator>Moh'd, Fatma Hilali</dc:creator>
	<dc:creator>Maghfiroh, Firda Aulia</dc:creator>
	<dc:creator>Notodiputro, Khairil Anwar</dc:creator>
	<dc:creator>Angraini, Yenni</dc:creator>
	<dc:description xml:lang="en-US">The impact of the COVID-19 pandemic on the air transportation sector, particularly Soekarno-Hatta (Soetta) International Airport, has been quite significant. The number of passengers at Soetta Airport has decreased due to the COVID-19 pandemic, but flight activities are still ongoing to this day. An accurate forecasting model is needed to predict the number of airline passengers at Soetta Airport with the presence of the COVID-19 pandemic as an intervention. In this study we discuss performance comparison of two models namely SARIMA intervention and Prophet in forecasting the number of domestic passengers at Soetta Airport. The research results showed that the best SARIMA intervention model was SARIMA (0,1,1)(1,0,0)12 b = 0, s = 20, r = 0, with a Mean Absolute Percentage Error (MAPE) of 28% and Root Mean Square Error (RMSE) of 433473. On the other hand, the Prophet model yielded a MAPE of 37% and an RMSE of 497154. In terms of MAPE and RMSE, the SARIMA intervention method provides better results than the Prophet model in forecasting the number of domestic passengers at Soetta Airport.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-12-19</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/9593</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss4pp2107-2120</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications; 2107-2120</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss4year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/9593/7196</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Vivin Nur Aziza, Fatma Hilali Moh'd, Firda Aulia Maghfiroh, Khairil Anwar Notodiputro, Yenni Angraini</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>