<![CDATA[Indonesia holds vast potential for creative tourism through its rich cultural heritage, natural beauty, and local creativity. However, travelers still face challenges in planning optimal trips due to the lack of context-aware and real-time recommendation systems. In practice, tourists often rely on Google Maps reviews, which are unorganized thematically, and there is limited integration with weather conditions—an important factor that significantly impacts travel experiences, particularly for nature-based destinations. This study aims to develop a recommendation model for creative tourism destinations in Indonesia by integrating two key aspects: sentiment analysis of Google Maps reviews and real-time weather data. The research utilizes tourist reviews from Google Maps alongside up-to-date weather information from destinations across Indonesia. The reviews are analyzed using the Support Vector Machine (SVM) algorithm to classify sentiments as positive or negative. These sentiment results are then combined with real-time weather data to build a Content-Based Filtering (CBF) recommendation system capable of providing more relevant and adaptive suggestions. The study successfully produced a recommendation system model with a testing accuracy of 90%.]]>
Copyrights © 2025
