Penerapan Metode Content-Based Filtering Pada Rekomendasi Produk di Meubel Pratama Mulya

Abstract

The furniture industry faces challenges in helping customers find products that meet their needs on digital platforms because existing search features are still general. Meubel Pratama Mulya, a furniture store with a diverse collection of materials and design styles, does not yet have a mechanism to automatically suggest products to customers. This study developed a web-based furniture product recommendation system using the Content-Based Filtering (CBF) method to analyze product similarities based on material and design style attributes. The dataset used consisted of 40 furniture products. The system was proven to be able to generate relevant recommendations based on the highest similarity value between the desired product and the available catalog. Algorithm performance testing showed an average Precision value of 85% and Accuracy of 91%, indicating that the system is able to provide recommendations based on the characteristics of the product sought by users. This system is equipped with an admin dashboard with real-time data trend visualization. With this system, product search efficiency and user experience in the Meubel Pratama Mulya digital catalog are expected to improve.