<![CDATA[This study investigates the application of Google Trends data as regressor variables for nowcasting passenger arrivals at Komodo Airport, the primary gateway to Labuan Bajo, a key tourism destination in Indonesia. Utilizing machine learning, the research aims to enhance the accuracy of passenger arrival prediction while deriving insights related to tourism interest in Labuan Bajo. The analysis reveals a significant correlation between trending search terms and actual passenger arrivals, indicating that shifts in online interest can effectively predict tourist behavior. Additionally, specific search queries reflecting potential visitors' preferences are identified, providing valuable insights for tourism stakeholders and marketing professionals. The findings underscore the relevance of passenger arrival data as a critical indicator of tourism activity, assisting policymakers and businesses in making informed decisions to enhance the tourist experience in Labuan Bajo. By utilizing Google Trends data in the nowcasting process, this research contributes to the evolving discourse on data-driven strategies in tourism management and highlights the potential of leveraging big data to support sustainable tourism development in Labuan Bajo, aligning with government efforts to promote the region as a premier travel destination in Indonesia.]]>
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