Forecasting Post-COVID-19 Tourist Arrivals to Indonesia: Seasonal ARIMA vs Random Forest
DOI:
https://doi.org/10.67955/bsij.v1i2.23Keywords:
ARIMA, Random Forest, COVID-19, Tourism Forecasting, Indonesia, Time Series, Machine LearningAbstract
Indonesia's international tourism sector, one of the country's foremost foreign exchange contributors, suffered a near-total collapse when the COVID-19 pandemic took hold in early 2020, with arrivals plunging by approximately 75% relative to 2019's baseline. The recovery that followed was highly non-linear shaped by uneven border reopening timelines, shifting travel restrictions, and residual traveler hesitancy introducing volatility patterns that stretch far beyond the bounds of normal seasonal fluctuation. This study examines how two methodologically distinct forecasting approaches, the Autoregressive Integrated Moving Average (ARIMA) and Random Forest (RF), perform under these conditions, using 72 months of official BPS Indonesia data spanning January 2019 to December 2024, with 2019–2023 as the training window and 2024 reserved as the holdout test period. ARIMA modeling followed the Box-Jenkins procedure, yielding an optimal SARIMA(1,1,1)(1,1,1)12 specification. The Random Forest model was built upon 18 engineered autoregressive lags, rolling window statistics, and cyclical month encodings with hyperparameters tuned through GridSearchCV. Empirical validation on the 2024 test set revealed that the parametric ARIMA model significantly outperformed the machine learning approach, achieving a MAPE of 5.49% compared to 8.66% for Random Forest, representing a 36.6% reduction in relative percentage error, alongside substantial improvements in MAE (42.7%) and RMSE (21.9%). Feature importance analysis indicated that short-term temporal dependencies (lag_1 and lag_2) heavily dominate the tree ensemble, which fundamentally limited its capability to extrapolate the expanding post-pandemic growth trend. These results suggest that classical parametric frameworks with proper regular and seasonal differencing offer more reliable mid-term demand estimates for tourism planners and policymakers navigating structural recovery trajectories.
References
Ampountolas, A. (2021). Forecasting hotel demand uncertainty using time series Bayesian VAR models. Tourism Economics, 27(5), 1102–1127. https://doi.org/10.1177/1354816620913790
Andariesta, D. T., & Wasesa, M. (2022). Machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic: A multisource data approach. Journal of Tourism Futures, 10(2), 232–251. https://doi.org/10.1108/JTF-05-2021-0130
Badan Pusat Statistik Indonesia. (2020). Statistik Indonesia 2020. BPS Indonesia. https://www.bps.go.id
Badan Pusat Statistik Indonesia. (2022). Statistik Kunjungan Wisatawan Mancanegara 2021. BPS Indonesia. https://www.bps.go.id
Badan Pusat Statistik Indonesia. (2024). Perkembangan Pariwisata dan Transportasi Nasional, Desember 2024. https://www.bps.go.id
Cankurt, S., & Subasi, A. (2016). Developing tourism demand forecasting models using machine learning techniques with trend, seasonal, and cyclic components. Balkan Journal of Electrical and Computer Engineering, 4(1), 19–25.
Fauzan Dwi Firmansyah & Muhammad Purnama Saddam. (2026). Trust and Usefulness in Students’ Acceptance of AI-Based Learning: Evidence from Jakarta High Schools. Business System & Innovation Journal, 1(1), 19–25. https://doi.org/10.67955/bsij.v1i1.10
Gunter, U., & Önder, I. (2015). Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data. Tourism Management, 46, 123–135. https://doi.org/10.1016/j.tourman.2014.06.017
Hawari, M. F., Zali Pauzan, & Hawwari, A. (2026). Social Media and Fintech Utilization on SME Development: The Mediating Role of Business Performance in Pekanbaru, Riau. Business System & Innovation Journal, 1(1), 1–8. https://doi.org/10.67955/bsij.v1i1.3
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning: With Applications in R. https://doi.org/10.1007/978-1-0716-1418-1
Li, G., Song, H., & Witt, S. F. (2005). Recent developments in econometric modeling and forecasting. Journal of Travel Research, 44(1), 82–99. https://doi.org/10.1177/0047287505276594
Li, G., Wu, D., Zhou, M., & Liu, A. (2018). The combination of interval forecasts in tourism. Annals of Tourism Research, 75, 363–378. https://doi.org/10.1016/j.annals.2019.01.010
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.
Pramita, D. A. K., & Saraswati, N. W. (2026). Forecasting hotel demand with time series prediction model using Random Forest Regression. Sinkron: Jurnal Dan Penelitian Teknik Informatika, 10(1), 112–124. https://doi.org/10.33395/sinkron.v10i1.11246
Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 1394–1401. https://doi.org/10.1109/ICMLA.2018.00227
Song, H., Qiu, R. T. R., & Park, J. (2019). A review of research on tourism demand forecasting. Annals of Tourism Research, 75, 338–362. https://doi.org/10.1016/j.annals.2019.04.016
Soraya, S., Nurhidayati, M., & Herawati, B. (2021). Forecasting foreign tourist visits to West Nusa Tenggara using ARIMA method. Jurnal Varian, 4(2), 89–98.
UNWTO. (2020). World Tourism Barometer (Vol. 18). https://www.unwto.org
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhammad Nabil Alfarizi, Susmanto Lesmana Putra, Nur Fadhilah Ilyas (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
