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Tourism is a vital sector for foreign exchange earnings, and the arrival of international tourists plays a significant role in Indonesia's economy. To manage the fluctuations in tourist numbers and enhance services, accurate forecasting of tourist arrivals is essential. This study aims to optimize the forecasting model using a hybrid LSTM-PSO approach to predict tourist arrivals accurately. By utilizing Google Trends data, this model is expected to provide better prediction results, assisting the government and the tourism industry in resource planning, budget management, and marketing strategies, as well as enhancing the tourist experience.

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Enhancing Tourist Arrival Forecasts Through Hybrid PSO and LSTM Methods

  • 章节

  • 发表日期：

    2026年

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  • Harun Mukhtar,
  • Muhammad Akmal Remli,
  • Khairul Nizar Syazwan Wan Salihin Wong,
  • Farhan Ridhollah,
  • Raihana Nasution,
  • Yulia Fatma,
  • Reny Medikawati Taufiq,
  • Andes Fuady Dharma Harahap

摘要

Tourism is a vital sector for foreign exchange earnings, and the arrival of international tourists plays a significant role in Indonesia's economy. To manage the fluctuations in tourist numbers and enhance services, accurate forecasting of tourist arrivals is essential. This study aims to optimize the forecasting model using a hybrid LSTM-PSO approach to predict tourist arrivals accurately. By utilizing Google Trends data, this model is expected to provide better prediction results, assisting the government and the tourism industry in resource planning, budget management, and marketing strategies, as well as enhancing the tourist experience.