Mobile-Based Book Recommendation System Based on Film Preferences Using Content-Based Filtering - Nabil Anshari; Retno Mumpuni; Budi Mukhamad Mulyo (bit-Tech, 2025)
The low reading interest among the Indonesian population remains one of the main challenges in improving national literacy quality. One contributing factor is the difficulty in finding reading materials that align with individual interests. Conversely, the increasing public interest in films can be leveraged as a bridge to foster reading habits. This study discusses the development of a mobile-based book recommendation system based on users’ film preferences to facilitate the discovery of books relevant to their favorite films. The proposed method here employs a Content-Based Filtering approach using Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity to measure the similarity between film synopsis and book descriptions. Data are retrieved in real time through the integration of The Movie Database (TMDB) API and Google Books API. System evaluation was conducted using User Acceptance Testing (UAT) with ISO 9126 as the evaluation framework, focusing on functionality, usability, and reliability aspects. The results show that the application successfully provides relevant book recommendations based on users’ selected films, achieving functionality, usability, and reliability scores of 88%, 84%, and 86%, respectively. Therefore, the system is considered feasible for use and has the potential to serve as a literacy enhancement medium based on film preference.