  Tourism data is one of the strategic data in Indonesia. In addition, tourism
is one of the ten priority programs of national development planning in
Indonesia. BPS-Statistics Indonesia has collected data related to tourism
demand in Indonesia, but these data have different time period. Several data
can be provided monthly, while the other data can be provided annually.
However, accurate and real time tourism data are needed for effective policy
making. In this era, all of information about tourism destination or
accommodation can be gotten easily through internet, especially information
from Google search engine, such as information about tourism places, flights,
hotels, and ticket for tourism attractions. Since 2004, Google has provided the
information of user behavior through Google Trends tool. This paper aims to
analyze and compare the patterns of tourism demand in Indonesia from Google
Trends data with tourism statistics from BPS-Statistics Indonesia. In order to
understand tourism demand in Indonesia, we used Google Trends data on a set of
queries related to tourism. This paper shows that the search intensity of
related 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 official
data. The result shows that Google Trends data and tourism statistics have
similar pattern when there were disasters. The result also shows that Google
Trends data has correlation with official data and produced accurate prediction
of tourism demand in Indonesia. Therefore, Google Trends data can be used to
predict and understand the pattern of tourism demand in Indonesia.
