Implementasi dan Evaluasi Explainable Recommender System pada Pemilihan Sepeda Motor Menggunakan Weighted Content-Based Filtering Berbasis Web - Yulia Darmi; Abiyyu Rahim; Rafly Nabil Lathif; Ramadhana Abdul Rasyid; Alifio Eren Atmaja; Yoga Andesmi; Tri Monica (Pusat Riset dan Inovasi Nasional, 2026)
The increasing number and variety of motorcycles available in the market often make it difficult for prospective buyers to select a motorcycle that matches their needs and preferences. This study aims to implement and evaluate an Explainable Recommender System for motorcycle selection using the Weighted Content-Based Filtering method in a web-based environment. The proposed system utilizes motorcycle specification data and user preference profiles consisting of budget, motorcycle type, engine capacity, fuel efficiency priority, luggage capacity requirement, user height, daily usage, and touring needs. Criterion weights were obtained from questionnaire responses collected from 40 respondents and normalized before being applied in the recommendation process. Recommendation results were generated by calculating the similarity between user preferences and motorcycle specifications, followed by a weighted scoring mechanism to rank available alternatives. To improve transparency, the system provides explanations regarding the factors that influence each recommendation result. System evaluation was conducted through Black Box Testing, User Acceptance Test (UAT), and explainability evaluation involving 60 respondents. The Black Box Testing results showed that all system functions operated successfully with a success rate of 100%. The UAT evaluation achieved a score of 84.75%, while the explainability evaluation obtained a score of 81.50%, indicating that users positively perceived the usability, recommendation relevance, and explanation quality of the system. In addition, user satisfaction evaluation resulted in a score of 79.83%, indicating that the system is considered useful and feasible as a decision-support tool. The findings demonstrate that the integration of Weighted Content-Based Filtering and explainable recommendation features can generate personalized motorcycle recommendations while improving user understanding and trust in the recommendation process.