Sistem Rekomendasi Ular Peliharaan Berbasis Web dengan Algoritma Content-Based Filtering - Abiyyu Dhonan Pratomo; Ucta Pradema Sanjaya (Institut Teknologi Garut, 2026)
This study developed a web-based pet snake recommendation system using a Content-Based Filtering (CBF) algorithm with a risk-aware approach to help prospective owners choose snake species that suit their profiles, while reducing negative bias against snakes. This system maps user attributes and snake species through ANY-match, exact-match, and multi-level fallback mechanisms that integrate risk-based hard filters, including venom level, aggressiveness, and body size. This study adds scientific contributions in the application of risk-sensitive CBF, where recommendations are prioritized based on user safety. Weight sensitivity tests, comparisons with baseline rule-based systems, and validation with snake keeper experts confirm the system's reliability. User Acceptance Testing (UAT) results show that the system has an average user satisfaction rating of 4.84 out of 5, with 97.47% of respondents giving positive ratings. Although this system has been proven effective in providing relevant and safe recommendations, the limitations of this study lie in the limited number of species and respondents, as well as the scale of testing, which still needs to be expanded. This study is expected to serve as the basis for the development of risk-based recommendation systems for other exotic animals.