KNOWLEDGE-BASED HIJAB PRODUCT SELECTION RECOMMENDATION SYSTEM AT CANDY SCARVES

The primary objective of this study is to construct a knowledge-driven hijab product selection recommendation system for Candy Scarves. This system is designed to help customers find hijabs that match their criteria by utilizing customer characteristics and product attributes. The study uses a knowledge-based recommendation approach supported by case-based techniques. The construction of the system is orchestrated through the application of the Rapid Application Development (RAD) paradigm, encompassing a sequence of iterative stages—ranging from requirement formulation and architectural design to accelerated prototyping and eventual deployment—thus privileging adaptability and user-centered refinement over linear progression. Data modeling using sample data totaling 25 hijab products and 6 attributes. The system provides recommendations based on criteria for hijab models, materials, hijab colors, skin colors, motifs, and prices. The empirical findings reveal that the hijab item exhibiting the utmost degree of similarity is the Umama Hijab with voal material, mocha hijab color, brown skin color, and plain motifs with a result of 0.90303. The results of this analysis are able to provide personal recommendations effectively and have the potential to increase customer satisfaction and product sales.