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Tourism data is one of the strategic data in Indonesia. In addition, tourismis one of the ten priority programs of national development planning inIndonesia. BPS-Statistics Indonesia has collected data related to tourismdemand in Indonesia, but these data have different time period. Several datacan be provided monthly, while the other data can be provided annually.However, accurate and real time tourism data are needed for effective policymaking. In this era, all of information about tourism destination oraccommodation can be gotten easily through internet, especially informationfrom Google search engine, such as information about tourism places, flights,hotels, and ticket for tourism attractions. Since 2004, Google has provided theinformation of user behavior through Google Trends tool. This paper aims toanalyze and compare the patterns of tourism demand in Indonesia from GoogleTrends data with tourism statistics from BPS-Statistics Indonesia. In order tounderstand tourism demand in Indonesia, we used Google Trends data on a set ofqueries related to tourism. This paper shows that the search intensity ofrelated queries provides the pattern of predicted tourism demand in Indonesia.We evaluated the prediction result by comparing several time series models.Furthermore, we compared and correlated the Google Trends data with officialdata. The result shows that Google Trends data and tourism statistics havesimilar pattern when there were disasters. The result also shows that GoogleTrends data has correlation with official data and produced accurate predictionof tourism demand in Indonesia. Therefore, Google Trends data can be used topredict and understand the pattern of tourism demand in Indonesia.

Predicting Tourism Demand in Indonesia Using Google Trends Data