ANALYSIS INTEGRATION OF GIS AND SOCIAL MEDIA TO IDENTIFY VIRAL TOURISM TRENDS IN TOURISM DESTINATIONS - Supiyandi, Supiyandi; Mailok, Ramlah Binti (LPPM UNIVERSITAS BINA INSAN, 2025)
This research aims to integrate the Geographic Information System (GIS) and social media in identifying viral culinary tourism trends and analyzing factors that contribute to the virality of a culinary destination. The study uses a mixed-methods approach, combining GIS-based spatial analysis with social media sentiment analysis from platforms like Instagram, TikTok, and Google Reviews. Spatial data is collected through Google Maps APIs and OpenStreetMap, while social data is obtained through web scraping and social media APIs. The analysis techniques used include geotagging, spatial clustering (K-Means and DBSCAN), and sentiment analysis based on Natural Language Processing (NLP). Viral culinary tourism has a specific spatial pattern, most of which are in city centers and areas with high accessibility. Social media plays a major role in the virality of culinary destinations, with Instagram (45%) and TikTok (35%) as dominant platforms. The main factors determining virality are visual appeal, accessibility, influencer recommendations, and unique customer experience. The positive impacts of culinary tourism virality include increasing visits by up to 40% and local economic growth. In contrast, the negative impacts include over-tourism, price spikes, and decreased service quality. GIS can be used as a strategic tool in culinary tourism management, especially in predicting trends and optimizing the distribution of tourists to prevent overcrowding. Integrating GIS and social media has proven to be effective in analyzing and predicting viral culinary tourism trends. The results of this study provide insight into the government, business actors, and tourists in managing and responding to the tourism virality phenomenon more adaptively and sustainably. This study recommends the application of AI and machine learning in culinary tourism data analysis to improve the accuracy of predicting future trends.