Online Learning Video Recommendation System Based on Course and Sylabus Using Content-Based Filtering

Learning using video media such as watching videos on YouTube is an alternative method of learning that often used. However, there are so many available finding with the right content difficult and time-consuming. Therefore, this study builds a recommendation system can recommend based courses syllabus. The works by looking for similarity between syllabus annotations cosine method. annotation title description captured in real-time from API. This will produce recommendations form five selected test results show average performance percentage 81.13% achieving goals, namely relevance, novelty, serendipity increasing diversity.