Deep Learning With Processing Algorithms for Forecasting Tourist Arrivals

Harun Mukhtar, Muhammad Akmal Remli, Khairul Nizar Syazwan Wan Salihin Wong, Mohd Saberi Mohamad

Research output: Contribution to journalArticlepeer-review


The DL (Deep Learning) method is the standard for forecasting tourist arrivals. This method provides very good forecasting results but needs improvement if the data is small. Statistical data from the BPS (Central Bureau of Statistics) needs to be corrected, resulting in forecasts that tend to be invalid. This study uses statistical data and GT (Google Trends) as a solution so that the data is sufficient. GT data has a lot of noise because there is a shift between web searches and departures. This difference will produce noise that needs to be cleaned. We use monthly data from January 2008 to December 2021 from BPS sources combined with GT. Hilbert-Huang Transform (HHT) is proposed to clean data from various disturbances. The DL used in this study is long short-time memory (LSTM) and was evaluated using the root mean squared error RMSE and mean absolute percentage error (MAPE). The evaluation results show that the HHT-LSTM results are better than without data cleaning.

Original languageEnglish
Pages (from-to)1742-1753
Number of pages12
JournalTEM Journal
Issue number3
Publication statusPublished - Aug 2023


  • data
  • Deep learning (DL)
  • Google trends (GT)
  • HHT
  • long short-time memory (LSTM)
  • tourism arrivals

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems
  • Education
  • Strategy and Management
  • Information Systems and Management
  • Management of Technology and Innovation


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