<![CDATA[The purpose of this study is to apply the Knowledge based recommendation method for typical Solo food. This method is used to provide users with accurate and relevant recommendations about famous Solo specialties. This study uses a knowledge-based approach to collect and organize information about the legendary dish, including its properties, ingredients, method of preparation, and where it is served. the research phase involves identifying legendary dishes through literature studies and interviews with culinary experts, gathering knowledge about these dishes, organizing the knowledge into recommender systems, and developing appropriate algorithms or methods for knowledge-based recommendations. The recommendation system developed is evaluated and verified using test data and user feedback. The results of this study aim to provide legendary nutritional recommendations that are relevant and in accordance with user preferences. Using the Knowledge-based recommendation methodology, users can better discover and explore Solo culinary delights. The benefit of Knowledge based recommendation is the ability to set user priority levels based on user needs by calculating the similarity score between customer needs and food attributes. Knowledge based recommendation modeling for food choice recommendation systems can provide five search attributes for food product choices, namely type of food, price, ingredients, full menu, and instructions. Based on the results of the Knowledge-based recommendation modeling method with 10 data samples, food recommendations can be given according to the criteria required by customers by calculating the similarity value between customer needs and the attributes of each food. Foods with the highest similarity values are displayed according to food recommendations, namely. H. The highest similarity score is 0.77 for Soto Gading food. The results of this Knowledge-based recommendation model can be used as a reference in developing a legendary food selection recommendation system in Solo]]>
