<![CDATA[Choosing the right cat food is often an obstacle for Giant Pet shop customers due to the many product variations and monotonous recommendations from sellers. This study designs a mobile application-based cat food recommendation system by implementing a knowledge-based method and a constraint-based approach. The system allows users to receive product suggestions based on brand attributes, taste, cat age, weight, price, and type of food. Data collection was carried out through literature studies and direct observations at Giant Pet shop, with system development following the waterfall model including needs analysis, UML design, and implementation. The results of the system test showed very good performance with a precision of 88.90% which proves the accuracy of recommendations according to user criteria, and a recall of 100% which shows completeness in displaying all relevant products. These results confirm that the system can help and make it easier for users to find products that suit their needs.]]>
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