<![CDATA[Content-Based Filtering is a user-independent method, meaning it does not rely on whether an item is new or previously selected by other users. This study aims to design and develop a recommendation system for iconic culinary places in Solo City using the Content-Based Filtering method. The system helps tourists find culinary options based on individual preferences such as food name, rating, and price. Culinary data was collected through web scraping from Google Maps using the Instant Data Scraper extension. The data is processed using the TF-IDF algorithm and cosine similarity to calculate the similarity between content features. The system development follows the Rational Unified Process (RUP) with four phases: inception, elaboration, construction, and transition. It is built using PHP with the Laravel framework and MySQL database. The system provides a list of culinary recommendations complete with images, names, ratings, addresses, and prices. Black-box testing on six main scenarios showed 100% success, proving the system meets functional requirements. The final recommendation results show a similarity score above 70%, indicating accurate and relevant suggestions. This system helps users discover Solo's iconic culinary spots more efficiently and according to their preferences.]]>
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