The Impact of Online Reviews to Predict The Number of International Tourists

Zhasa Vashellya; Erna Nurmawati; Teguh Sugiyarto

doi:10.15575/join.v10i1.1409

Authors

Zhasa Vashellya Computational Statistics Study Program, STIS Polytechnic of Statistics, Indonesia
Erna Nurmawati Computational Statistics Study Program, STIS Polytechnic of Statistics, Indonesia
Teguh Sugiyarto Directorate of Finance, Information Technology, Tourism Statistics, BPS-Statistics, Indonesia

DOI:

https://doi.org/10.15575/join.v10i1.1409

Keywords:

International tourists, Online Review, Sentiment Analysis, Prediction, LSTM

Abstract

The tourism sector is a potential resource for advancing the Indonesian economy. The development of the tourism industry is represented by the number of international tourist arrivals. Therefore, this indicator becomes an objective in development programs. To accomplish this goal and assess the demand aspect of the tourism sector, it is a must to have a precise forecast of the number of international visitors. This research attempts to develop precise methods and models for estimating the number of international tourists based on this premise. This study chooses Bali Province as its focus since nearly half, or 47%, of the tourists who visit Indonesia arrive through the entry point in Bali Province. This research uses the LSTM method and big data online reviews in building prediction models. The results of this study show that sentiment analysis of tourist attractions in Bali using the BERT model has an accuracy of 75%. The results also depict that reviews by visitors about tourist attractions in Bali Province during the period 2012-2023 contain more positive sentiments. Furthermore, the best model to predict the number of international tourists, with the smallest RMSE and MAPE values (39,470.64 and 11.25%, respectively), includes inflation, rupiah exchange rates, TPK, monthly sentiment scores, and the number of reviews as dependent variables. The prediction model also show that the review variables (sentiment score and number of reviews) can improve prediction accuracy.

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