Forecasting Tourist Visits During The Covid-19 Pandemic and MotoGP Events Using The Sarima Method

Siti Soraya, Phyta Rahima, Gilang Primajati, Maulida Nurhidayati, Mohammad Fajri

Abstract


The 5.0 era has made the tourism sector one of the measures of the economic welfare of a region, such as in West Nusa Tenggara (NTB). This is proven by the presence of various types of MSMEs and their innovations and the increasing number of tourist visits to NTB from year to year. The condition of the tourism sector certainly has a positive impact on increasing NTB's economic growth and indirectly on optimizing existing infrastructure. However, extraordinary events such as the earthquake in 2018 and the COVID-19 pandemic resulted in the decline of NTB tourism visits. Then tourist visits in NTB increased again with the holding of the MotoGP  Event. The purpose of this study is to forecast the number of tourist visits to NTB. This is very much needed in helping the government to prepare appropriate policies if there is a possibility of a surge in tourist visits in the following years. As well as anticipating if there are other extraordinary events such as earthquakes or global cases. The method used in this study is the Seasonal Autoregressive Integrated Moving Average (SARIMA) Method. The stages in this method are by describing data, preprocessing data, identifying stationary models, estimating models, selecting the best SARIMA model and forecasting with the obtained model to forecasting the next desired period. The results of research that have been conducted state that in 2023 to 2024 the number of tourists visiting NTB continues to increase both domestically and abroad. It is hoped that the results of this research will be able to provide information and contribute knowledge and consideration materials in policy making in the development of NTB government tourism.

Keywords


Touriszm; Forecasting; SARIMA; NTB; MOTOGP; Covid-19

Full Text:

PDF

References


Valguna PA, Dewanti D, Suparma LB. Dampak Perkembangan Pariwisata Pulau Lombok terhadap Pengembangan Bandar Udara Internasional Lombok. J Manaj Aset Infrastruktur Fasilitas. 2020;4(3):195–210.

Moerwanto AS, Junoasmono T. Strategi Pembangunan Infrastruktur Wisata Terintegrasi. J HPJI. 2017;3(2):67–78.

Irfan P, Apriani A. Analisa Strategi Pengembangan E-Tourism Sebagai Promosi Pariwisata Di Pulau Lombok. Ilk J Ilm. 2017;9(3):325–30.

Kadarisman A. Government public relations dalam pengembangan pariwisata masa pandemi COVID-19 di Geopark Ciletuh. PRofesi Humas J Ilm Ilmu Hub Masy. 2021;5(2):270.

Hayatun Sofian, Yulanda Trisula SY, Sandi Justitia Putra, Lalu Ahmad Rahmat, I Made Putra Suryantara. Analisis Agenda Setting Pemberitaan MotoGP Mandalika Pada RRI Mataram. MUKASI J Ilmu Komun. 2022;1(1):11–20.

Herawati S. Peramalan Kunjungan Wisatawan Mancanegara Menggunakan Generalized Regression Neural Networks. J InfoteL - Inform Telekomun Elektron. 2016;8(1):35.

Rahman NHA, Lee MH, Suhartono, Latif MT. Evaluation Performance of Time Series Approach for Forecasting Air Pollution Index in Johor, Malaysia. Sains Malaysiana. 2016;45(11):1625–33.

Silalahi DK. Forecasting of Poverty Data Using Seasonal ARIMA Modeling in West Java Province. JTAM | J Teor dan Apl Mat. 2020;4(1):76.

Multiningsih M, Siswanah E, Saleh M. Forecasting the Number of Ship Passengers with SARIMA Approach (A Case Study: Semayang Port, Balikpapan City). JTAM (Jurnal Teor dan Apl Mat. 2022;6(4):1060.

Kasus S, Hujan C, Ambon K, Maluku P, Kafara Z, Rumlawang FY, et al. Peramalan Curah Hujan Dengan Pendekatan Seasonal Autoregressive Integrated Moving Average ( Sarima ) Rainfall Forecasting Using Seasonal Autoregressive Integrated Moving Average Model ( Sarima ). 2017;11:63–74.

Ilmayasinta N. Forecasting Arrival of Foreign Tourists Using Seasonal Arima Box-Jenkins. BAREKENG J Ilmu Mat dan Terap. 2021;15(2):223–30.

City S, City S, City S, Tahir HA, Ahmed RA, Mhamad AJ. Forecasting for the Imported Weight of Equipment to Cargo of Sulaimani International Airport. 2019;2520(3):62–82.

Eni D, Adeyeye FJ. Seasonal ARIMA Modeling and Forecasting of Rainfall in Warri Town, Nigeria. J Geosci Environ Prot. 2015;03(06):91–8.

Didiharyono D, Bakhtiar B. Forecasting Model With Box-Jenkins Method To Predict Tourists Who Visit Tourism Place In Toraja. J Econ Manag Account. 2018;1(1):34–41.

Chuang A, Wei WWS. Time Series Analysis: Univariate and Multivariate Methods. Vol. 33, Technometrics. 1991. p. 108.

Annisa Fitri, Ika Purnamasari MS. Peramalan jumlah wisatawan mancanegara menggunakan model arima. Statistika. 2019;7(1).

Zhang Y, Luo L, Yang J, Liu D, Kong R, Feng Y. A hybrid ARIMA-SVR approach for forecasting emergency patient flow. J Ambient Intell Humaniz Comput. 2019;10(8):3315–23.

Arini PS, Nawangsih E. Peramalan Jumlah Kunjungan Wisatawan Mancanegara (Wisman) ke Bali Tahun 2019: Metode ARIMA. 2019;136–41.

Didiharyono D, Syukri M. Forecasting with arima model in anticipating open unemployment rates in south sulawesi. Int J Sci Technol Res. 2020;9(3):3838–41.

Li W, Zhang ZG. Based on time sequence of ARIMA model in the application of short-term electricity load forecasting. ICRCCS 2009 - 2009 Int Conf Res Challenges Comput Sci. 2009;11–4.

Iriani NP, Akbar MS. Estimasi Value at Risk ( VaR ) pada Portofolio Saham dengan Copula. 2010;(3):1–6.

Hendayanti NPN, Nurhidayati M. Perbandingan Metode Seasonal Autoregressive Integrated Moving Average (SARIMA) dengan Support Vector Regression (SVR) dalam Memprediksi Jumlah Kunjungan Wisatawan Mancanegara ke Bali. J Varian. 2020;3(2):149–62.




DOI: http://dx.doi.org/10.12962/j27213862.v7i3.20139

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License
Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.

ISSN:  0216-308X

e-ISSN: 2721-3862

Web Analytics Made Easy - StatCounter View My Stats