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The implementation of long-short term memory for tourism industry in Malaysia

Across the world, tourism is known as the largest contributor towards economy and the fastest developing industry. It has the capability of generating income, creating job opportunities and help people to understand the culture diversity of other countries. Therefore, tourism demand forecasting is r...

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Main Authors: Siti Aishah @ Tsamienah, Taib, Noratikah, Abu, Azlyna, Senawi
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2025
Subjects:
Q Science (General)
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/42125/1/The%20implementation%20of%20long-short%20term%20memory.pdf
http://umpir.ump.edu.my/id/eprint/42125/
https://doi.org/10.37934/araset.46.2.9097
https://doi.org/10.37934/araset.46.2.9097
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spelling my.ump.umpir.421252024-09-30T04:49:40Z http://umpir.ump.edu.my/id/eprint/42125/ The implementation of long-short term memory for tourism industry in Malaysia Siti Aishah @ Tsamienah, Taib Noratikah, Abu Azlyna, Senawi Q Science (General) QA Mathematics Across the world, tourism is known as the largest contributor towards economy and the fastest developing industry. It has the capability of generating income, creating job opportunities and help people to understand the culture diversity of other countries. Therefore, tourism demand forecasting is really needed to help the practitioners involved as well as government in pricing setting, in assessing future requirements of capacity to fulfil the customers’ demand or in making wise decisions on whether to explore new market or not. This study focuses on tourism demand forecasting based on the number of tourist arrival using recurrent neural network (RNN), which is long-short term memory (LSTM) model. The data used in this study is historical data of number of tourist arrivals in Malaysia before the onset of Movement Control Order (MCO) starting from January 2000 to February 2020 due to the COVID-19 outbreak. The data set was divided into two subsets, training and testing data sets based on ratio 80:20. The objective of this study is to determine an accurate forecasting model especially in tourism industry in Malaysia. The forecast evaluation implemented to predict the error of each model are Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) and the analyses for this model was performed by using Python software. Based on the results obtained, the LSTM model was considered as one of the accurate prediction methods for tourism demand in Malaysia due to the least error produced. It is hoped that these results can help the government as well as practitioners in tourism industry to make a right judgement and formulate better tourism plans in order to minimize any consequences in the future. Semarak Ilmu Publishing 2025-04 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/42125/1/The%20implementation%20of%20long-short%20term%20memory.pdf Siti Aishah @ Tsamienah, Taib and Noratikah, Abu and Azlyna, Senawi (2025) The implementation of long-short term memory for tourism industry in Malaysia. Journal of Advanced Research in Applied Sciences and Engineering Technology, 46 (2). pp. 90-97. ISSN 2462-1943. (Published) https://doi.org/10.37934/araset.46.2.9097 https://doi.org/10.37934/araset.46.2.9097
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic Q Science (General)
QA Mathematics
spellingShingle Q Science (General)
QA Mathematics
Siti Aishah @ Tsamienah, Taib
Noratikah, Abu
Azlyna, Senawi
The implementation of long-short term memory for tourism industry in Malaysia
description Across the world, tourism is known as the largest contributor towards economy and the fastest developing industry. It has the capability of generating income, creating job opportunities and help people to understand the culture diversity of other countries. Therefore, tourism demand forecasting is really needed to help the practitioners involved as well as government in pricing setting, in assessing future requirements of capacity to fulfil the customers’ demand or in making wise decisions on whether to explore new market or not. This study focuses on tourism demand forecasting based on the number of tourist arrival using recurrent neural network (RNN), which is long-short term memory (LSTM) model. The data used in this study is historical data of number of tourist arrivals in Malaysia before the onset of Movement Control Order (MCO) starting from January 2000 to February 2020 due to the COVID-19 outbreak. The data set was divided into two subsets, training and testing data sets based on ratio 80:20. The objective of this study is to determine an accurate forecasting model especially in tourism industry in Malaysia. The forecast evaluation implemented to predict the error of each model are Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) and the analyses for this model was performed by using Python software. Based on the results obtained, the LSTM model was considered as one of the accurate prediction methods for tourism demand in Malaysia due to the least error produced. It is hoped that these results can help the government as well as practitioners in tourism industry to make a right judgement and formulate better tourism plans in order to minimize any consequences in the future.
format Article
author Siti Aishah @ Tsamienah, Taib
Noratikah, Abu
Azlyna, Senawi
author_facet Siti Aishah @ Tsamienah, Taib
Noratikah, Abu
Azlyna, Senawi
author_sort Siti Aishah @ Tsamienah, Taib
title The implementation of long-short term memory for tourism industry in Malaysia
title_short The implementation of long-short term memory for tourism industry in Malaysia
title_full The implementation of long-short term memory for tourism industry in Malaysia
title_fullStr The implementation of long-short term memory for tourism industry in Malaysia
title_full_unstemmed The implementation of long-short term memory for tourism industry in Malaysia
title_sort implementation of long-short term memory for tourism industry in malaysia
publisher Semarak Ilmu Publishing
publishDate 2025
url http://umpir.ump.edu.my/id/eprint/42125/1/The%20implementation%20of%20long-short%20term%20memory.pdf
http://umpir.ump.edu.my/id/eprint/42125/
https://doi.org/10.37934/araset.46.2.9097
https://doi.org/10.37934/araset.46.2.9097
_version_ 1822924688074997760
score 13.235362

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