Comparison of The SARIMA Model and Intervention in Forecasting The Number of Domestic Passengers at Soekarno-Hatta International Airport
Keywords:Covid-19, intervention, number of passengers, SARIMA
The Covid-19 pandemic has had a massive effect on the air transportation sector. Soekarno-Hatta International Airport (Soetta) skilled a lower variety of passengers because of the Covid-19 pandemic, even though Soetta Airport persisted to perform normally. Forecasting the number of passengers needs to be done by the airport to decide the proper policy. Therefore, the airport wishes to estimate the range of passengers to determine the right coverage and prepare the facilities provided if there may be a boom withinside the range of passengers throughout the Covid-19 pandemic. Forecasting the number of domestic passengers at Soetta Airport on this examination makes use of the SARIMA model and intervention. This examination compares the SARIMA model and the intervention in forecasting the number of domestic passengers at Soetta Airport. The effects confirmed that the best SARIMA model became ARIMA ARIMA(0,1,0)(1,0,0)12 with MAPE and RMSE of 55,18% and 588887.4, respectively. The best intervention modelÂ became ARIMA0,1,1) (1,0,0)12 b = 0, s = 5, r = 1Â with MAPE of 35,25% and RMSE of 238563,4. The MAPE and RMSE values acquired suggest that the intervention model is better than the SARIMA model in forecasting the number of domestic passengers at Soetta Airport throughout the Covid-19 pandemic.
Anderson D, Sweeney D, Williams T. 2011. Statistics for Business and Economics. Canada: South-Western Cengage Learning.
BPS] Badan Pusat Statistik. 2020. Number of Passengers Flight at Main Airports (People) 2010â€“2020. [accessed 2021 Aug 17]. https://www.bps.go.id/indicator/17/66/2/sum-penumpang-plane-at-airport-utama.html
Daniel WW. 1989. Statistika Nonparametrik Terapan. Alih bahasa: Alex Tri KW. Jakarta: PT.Gramedia.
Durrah FI, Yulia Y, Parhusip TP, Rusyana A. 2018. Forecasting the Number of Airplane Passengers at Sultan Iskandar Muda Airport with the Method SARIMA (Seasonal Autoregressive Integrated Moving Average). J Data Anal. 1(1):1â€“11. doi:10.24815/jda.v1i1.11847.
Fahik DS, Jatipaningrum MT. 2021. Soekarno Hatta International Airport By Method Holt-Winters Exponential Smoothing and Seasonal ARIMA . J of Industrial and Computational Statistics. 6(1): 77â€“87.
Fitriani NL. 2018. Modeling the Jakarta Islamic Index Using the Multiple Input Transfer Function Model [thesis]. Bogor: IPB University.
Gujarati DN. 2003. Basic Econometrics. New York (US): McGraw-Hill.
Hyndman RJ, Athanasopoulos G. 2018. Forecasting: Principles and Practice. 2nd Editio. Melbourne: Otexts.Com. https://otexts.com/fpp2/.
Montgomery DC, Jennings CL, Kulahci M. 2008. Introduction to Time Series Analysis and Forecasting. Balding DJ, Cressie NAC, Fit GM, editor. New Jersey (US): Wiley.
Montgomery DC, Jennings CL, Kulahci Murat. 2015. Introduction Time Series Analysis and Forecasting. 2nd Editio. New Jersey (US): John Wiley & Sons.
Sukur moch halim, Kurniadi B, Haris, N Faradillahisari R. 2020. Handling of Health Services during the Covid-19 Pandemic Period in the Perspective of Health Law. J of Inicio Legis. 1(1): 1â€“17.
Sustrisno A, Rais, Setiawan I. 2021. Intervention Model Analysis The Number of Domestic Passengers at Sultan Hasanuddin Airports. Journal of Statistics. 1(1):41â€“49.doi:10.22487/27765660.2021.v1.i1.15436.
Wei W. 2006. Time Series Analysis Univariate and Multivariate Method. New York (US): Pearson Education.
Yaffee RA, McGee M. 2000. Introduction to Time Series Analysis and Forecasting with Applications of SAS and SPSS. New York (US): Academic Press.