The tourism sector in Indonesia is one of the economic sectors severely impacted by the COVID-19 epidemic and its variants. The government is attempting to revive the economy by implementing numerous recovery policies. These economic recovery policies, particularly in the tourism sector, must be backed by a policy evaluation conducted using tourism demand data, such as the number of international visitors visiting Indonesia. However, official data from the BPS-Statistics Indonesia has been released with a two-month delay and is sometimes revised the following month. Consequently, it is necessary to forecast the data for the current situation. In addition, the forecasting model must be modified to account for data conditions caused by the COVID-19 pandemic. This research proposes an ARIMAX forecasting model that utilizes Google Trends and an intervention forecasting model with data sources from BPS and Google Trends. Thus, this research aims to present an overview, develop a model and identify the best forecasting model, and calculate the influence of the intervention on the number of international visitors visiting Indonesia. Compared to the ARIMAX model, the results indicated that the intervention model provided the most accurate forecasts. Not only superior in forecasting results, but the intervention model also demonstrates the magnitude of the intervention's effect on the number of international visitors visiting Indonesia.