<![CDATA[This study develops a tourism destination recommendation system based on Content-Based Filtering integrated with geospatial feature analysis to enhance the relevance and contextual accuracy of recommendations for users. The system addresses the limitations of existing tourism recommendation platforms that primarily focus on popularity and ratings without considering users’ location proximity and personal preferences. The dataset used in this research originates from Tourism in Indonesia (Kaggle), focusing on the Jakarta and Bandung regions. Text features are extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the similarity between destinations is measured using Cosine Similarity. Additionally, geographic distances are analyzed through the Haversine formula to strengthen the spatial context of the recommendations. The system was developed using the Agile (Scrum) methodology to ensure an iterative and adaptive development process aligned with user needs. Evaluation results indicate strong system performance, achieving a Precision of 0.63, Recall of 0.90, and an F1-Score of 0.73. These findings demonstrate that integrating content-based and spatial analysis approaches effectively improves the accuracy and personalization of tourism recommendations based on users’ preferences and location context.]]>
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