<![CDATA[Abstract. News portals are an important source of information for the public, but the large number of articles often makes it difficult for readers to get the news they need. Therefore, this study aims to create and build a news recommendation system for Jatengupdates using Content-Based Filtering (CBF) to increase content relevance for users. System development uses the Agile Scrum approach and the CBF Algorithm uses TF-IDF (Term Frequency-Inverse Document Frequency), Vector Space Model (VSM), and Cosine Similarity to assess the similarity between articles based on keywords. To ensure that the system runs correctly, the system is tested using the Blackbox method. The test results show that all system components function well. The recommendation system achieved a relevance level of 85.71% based on user responses, with 33 out of 34 responses considered relevant. Therefore, the application of the Content-Based Filtering algorithm on the Jatengupdates news portal has proven effective in increasing content relevance as well as user engagement.]]>
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