<![CDATA[The rapid growth of e-commerce has led to an increasing number of motorcycle spare part products available across various online marketplaces. This condition often makes it difficult for users to select products that match their vehicle type, needs, and budget. This study aims to develop a motorcycle spare parts recommendation system using the Knowledge-Based Recommendation method to assist users in obtaining suitable product recommendations based on their stated preferences. The system was developed using the Python programming language on the Google Colab platform and utilized a dataset consisting of 100 motorcycle spare part products with attributes including product name, motorcycle compatibility, product category, price, and rating. The recommendation process was carried out by matching user preferences with product attributes, after which each product was scored using a weighted calculation with compatibility weighted at 45%, product category at 30%, price at 15%, and rating at 10%. The system was evaluated using 10 testing scenarios by assessing the Top-1 recommendation. The evaluation results showed that all testing scenarios successfully generated recommendations that matched user requirements, achieving an accuracy of 100%. The findings indicate that the implementation of the Knowledge-Based Recommendation method, combined with a weighted attribute mechanism based on motorcycle compatibility, product category, price, and rating, is capable of producing recommendations that align with user preferences without requiring users' purchase history or rating history. Furthermore, the proposed method was found to be effective for motorcycle spare parts recommendation systems and has the potential to assist users in selecting appropriate products more quickly and accurately.]]>
