Pemanfaatan Content Based Filtering untuk Rekomendasi Pencarian Literatur Digital pada Konten Keamanan Siber

Students require access to relevant literature to enhance their understanding of technological developments, particularly in the field of cybersecurity. However, many students face difficulties in finding literature that aligns with their interests and needs. This study aims to develop a digital literature recommendation system using the Content-Based Filtering (CBF) method. The system analyzes literature content, including titles and abstracts, to generate recommendations based on content similarity using the TF-IDF and Cosine Similarity methods. Data were collected through web scraping techniques from the Garuda Kemdikbud website, resulting in 115 literature documents. The system was tested using sample data to evaluate recommendation accuracy through precision and recall metrics. The results indicate that the CBF approach is effective in providing literature recommendations that match user preferences.