Big Data Analytics with Blockchain Technology for Understanding Tourist Preferences in Ecotourism - Ogi, Imelda W. J.; Sumual, Jacline I.; Pandowo, Merinda H. C.; Paat, Franda Benedicta; Tuegeh, Octavia D. M.; Mangindaan, Harley A. B.; Mintardjo, Christofell Mardy O. (Jurnal Ilmiah PLATAX, 2026)
This study examines the integration of big data analytics and blockchain technology to understand tourist preferences in the context of ecotourism. The research was conducted in Manado, Indonesia, and employed a mixed-methods design combining digital tourism data analysis, surveys, semi-structured interviews, and blockchain prototype implementation. The study analyzed 500 tourist reviews collected from major online platforms, involved 150 tourism SMEs as primary respondents, and piloted the proposed system with 50 selected SMEs. Big data analytics was used to identify dominant tourist preferences and segment visitors based on their behavioral patterns. At the same time, blockchain technology was implemented to improve the security, traceability, and integrity of preference data. The results revealed four major tourist segments: family travelers, solo travelers, young travelers, and international tourists, each characterized by different preference combinations related to accommodation, nature tourism, culinary experiences, and tourism services. The findings also showed that blockchain significantly strengthened data security by reducing recorded data leakage and violation cases to zero after implementation. In addition, SMEs that used preference-based insights were able to improve service personalization and reported positive business outcomes, particularly in accommodation and nature-based tourism services. User evaluation further indicated high levels of acceptance across ease of use, operational efficiency, data security, and personalization quality. Overall, the study demonstrates that integrating big data analytics and blockchain technology provides a valuable framework for delivering secure, data-driven, and personalized ecotourism services. Keywords: Big data analytics; Blockchain; Ecotourism; Service personalization; Tourist preferences