Forecasting The Number of ASEAN Tourists in Indonesia: The Impact of The COVID-19 Pandemic Using Intervention Analysis

Faizah, Nabilah Putri Noor; Budiarti, Retno; Agustiani, Nur

doi:10.20956/j.v22i2.48031

Open access Jurnal

Forecasting The Number of ASEAN Tourists in Indonesia: The Impact of The COVID-19 Pandemic Using Intervention Analysis

Jurnal Matematika, Statistika dan Komputasi · Vol. 22 No. 2 (2026) · pp. 363–379

DOI: 10.20956/j.v22i2.48031
ISSN: 2614-8811, 1858-1382
Published: 2026-01-10
Publisher: Department of Mathematics, Hasanuddin University
Language: en
License: creativecommons.org/licenses/by/4.0

Abstract

The COVID-19 pandemic has had a significant impact on Indonesia’s tourism sector, particularly on the number of tourist arrivals from ASEAN countries. International travel restrictions led to a drastic decline in visitor numbers. This study aims to forecast the number of ASEAN tourists visiting Indonesia using an intervention analysis based on the Autoregressive Integrated Moving Average (ARIMA) model to capture both the pandemic shock and the subsequent recovery phase. The data used are secondary data from Statistics Indonesia (BPS) covering the period January 2017–November 2024, with the training data divided into three phases: the pre-pandemic period (January 2017–January 2020), Intervention I or the pandemic period (February 2020–April 2022), and Intervention II or the recovery period (May 2022–December 2023). Testing data are used to evaluate forecasting performance for the period January 2024–November 2024. The results show that the ARIMA(2,1,0) model with a step-type intervention successfully captures significant changes in the data pattern, yielding a Mean Absolute Percentage Error (MAPE) of 14.91% on the training data—an improvement over both the non-intervention model (MAPE 86.31%) and the first intervention model (MAPE 56.68%). On the testing data, the model achieves even higher accuracy with a MAPE of 8.21%, indicating that the intervention model effectively represents the dynamics of the pandemic impact and the subsequent recovery.

Keywords

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Co-author	Organisation	Joint articles
Donny Citra Lesmana	Affiliation unknown	3
I Gusti Putu Purnaba	Affiliation unknown	2
Komang Nonik Afsari Dewi	Affiliation unknown	1
Vivin Aprilia Manjaruni	Affiliation unknown	1
Sanfriska Br Sitepu	Affiliation unknown	1
Romaito Br Silalahi	Affiliation unknown	1
Salsabilla Rahmah	Affiliation unknown	1
Ika Reskiana Adriani	Affiliation unknown	1
I Wayan Mangku	Affiliation unknown	1
Mohamad Khoirun Najib	Affiliation unknown	1
Alfi Khairiati	Affiliation unknown	1

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/jmsk/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.

Original metadata (oai_dc)

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	<dc:title xml:lang="en">Forecasting The Number of ASEAN Tourists in Indonesia: The Impact of The COVID-19 Pandemic Using Intervention Analysis</dc:title>
	<dc:creator xml:lang="en">Faizah, Nabilah Putri Noor</dc:creator>
	<dc:creator xml:lang="en">Budiarti, Retno</dc:creator>
	<dc:creator xml:lang="en">Agustiani, Nur</dc:creator>
	<dc:subject xml:lang="en">ARIMA</dc:subject>
	<dc:subject xml:lang="en">Covid-19</dc:subject>
	<dc:subject xml:lang="en">Intervention analysis</dc:subject>
	<dc:subject xml:lang="en">Forecasting</dc:subject>
	<dc:subject xml:lang="en">Tourism</dc:subject>
	<dc:description xml:lang="en">The COVID-19 pandemic has had a significant impact on Indonesia’s tourism sector, particularly on the number of tourist arrivals from ASEAN countries. International travel restrictions led to a drastic decline in visitor numbers. This study aims to forecast the number of ASEAN tourists visiting Indonesia using an intervention analysis based on the Autoregressive Integrated Moving Average (ARIMA) model to capture both the pandemic shock and the subsequent recovery phase. The data used are secondary data from Statistics Indonesia (BPS) covering the period January 2017–November 2024, with the training data divided into three phases: the pre-pandemic period (January 2017–January 2020), Intervention I or the pandemic period (February 2020–April 2022), and Intervention II or the recovery period (May 2022–December 2023). Testing data are used to evaluate forecasting performance for the period January 2024–November 2024. The results show that the ARIMA(2,1,0) model with a step-type intervention successfully captures significant changes in the data pattern, yielding a Mean Absolute Percentage Error (MAPE) of 14.91% on the training data—an improvement over both the non-intervention model (MAPE 86.31%) and the first intervention model (MAPE 56.68%). On the testing data, the model achieves even higher accuracy with a MAPE of 8.21%, indicating that the intervention model effectively represents the dynamics of the pandemic impact and the subsequent recovery.</dc:description>
	<dc:publisher xml:lang="en">Department of Mathematics, Hasanuddin University</dc:publisher>
	<dc:date>2026-01-10</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unhas.ac.id/index.php/jmsk/article/view/48031</dc:identifier>
	<dc:identifier>10.20956/j.v22i2.48031</dc:identifier>
	<dc:source xml:lang="en">Jurnal Matematika, Statistika dan Komputasi; Vol. 22 No. 2 (2026): JANUARY 2026; 363-379</dc:source>
	<dc:source>2614-8811</dc:source>
	<dc:source>1858-1382</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.unhas.ac.id/index.php/jmsk/article/view/48031/13990</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Jurnal Matematika, Statistika dan Komputasi</dc:rights>
	<dc:rights xml:lang="en">https://creativecommons.org/licenses/by/4.0</dc:rights>
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