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

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.

Authors

Institutions

Publication Details

Journal
Inferensi
Published
2024-11-28
DOI
https://doi.org/10.12962/j27213862.v7i3.20139
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Mohammad Chairul Fajri, Maulida Nurhidayati, Siti Soraya, Gilang Primajati et al.
Inferensi
Data Mining and Machine Learning Applications
article

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

Mohammad Chairul Fajri, Maulida Nurhidayati, Siti Soraya, Gilang Primajati, Phyta Rahima
article en

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.

InferensiVol. 7(3)
Tadulako University (ID), Institut Agama Islam Negeri Bengkulu (ID), Universitas Saburai (ID)
Openalex Percentile: Top 67%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Forecasting Tourist Visits During The Covid-19 Pandemic and MotoGP Events Using The Sarima Method — Mohammad Chairul Fajri, Maulida Nurhidayati, et al. · Inferensi (2024) | TGRS Research Map | TGRS