<![CDATA[Knowledge-based recommendation systems have become a crucial solution in assisting customers to select products that match their preferences, particularly in the garment industry. This study aims to develop a knowledge-based recommendation system for Deem Clothing's garment products, capable of addressing the challenges of direct product consultation. The study utilizes data obtained through interviews with the owner of Deem Clothing, direct business observations, and an analysis of the product catalog data. The method involves seven product criteria constraints: product type, material type, pattern, design details, color, additional accessories, and sleeve type. The recommendation process is conducted by implementing a simple constraint-based algorithm to generate product similarity scores and rank them from highest to lowest. The results indicate that the developed recommendation system can effectively and efficiently provide product recommendations that align with customer preferences. The conclusion of this study is that knowledge-based recommendation systems can reduce customer dependence on direct consultations, enhance the shopping experience, and optimize the sales process of garment products. The implications of this research for the field of knowledge are that knowledge-based approaches in recommendation systems can be widely applied across various industries to improve customer interaction and satisfaction.]]>
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