Optimalisasi Potensi Wisata NTT dari Perspektif Google Trends dan Big Data Analytics - Ellen Tantrisna; Ryan Peterzon Hadjon (Jurnal Teknologi Informasi dan Multimedia, 2025)
This study aims to identify and analyze tourism trends in East Nusa Tenggara (NTT) using the K-Means clustering method integrated with Google Trends and Big Data Analytics. By utilizing data that includes the number of tourist attractions, hotel accommodations, tourist visits (Domestic and foreign), and restaurant accomodation, the NTT region is categorized into several clusters based on tourism characteristics. The analysis results reveal three main clusters: areas with low tourist attractions and accommodations, areas with very high tourist attractions, and areas with good accommodation facilities but moderate attractions. These findings provide crucial insights for policymakers and tourism industry stakeholders to formulate more effective development strategies, such as infrastructure enhancement in high-potential areas and targeted promotion for niche markets. Additionally, the analysis results indicate significant fluctuations in tourist interest towards NTT, with peak searches occurring in April and September. This research utilizes data from Google Trends and other sources to analyze trends and tourist attractions in NTT tourism, thereby aiding in the development of more effective promotional strategies. Overall, this study contributes to a deeper understanding of tourism dynamics in NTT and the necessary optimization steps to enhance the competitiveness of these destinations. With this data-driven approach, it is hoped that the tourism sector in NTT can develop sustainably and provide economic benefits to local communities.