<![CDATA[The rapidly evolving job landscape in the digital era requires job seekers to continuously adapt to emerging skills driven by technological advancements across various industries. However, many job seekers struggle to keep up with these changing skill demands, while existing job portals often lack features that recommend relevant skills. To address this issue, this study proposes a skill recommendation system based on the User-Based Collaborative Filtering approach, which considers similarities between users’ preferences. Two similarity measurement methods, Log-Likelihood Similarity and Cosine Similarity, are applied and compared to evaluate their effectiveness. The system matches user skill profiles with skill requirements extracted from job vacancy data, where job postings are also treated as user representations. The dataset was collected from the Jobstreet job portal using web scraping techniques, ensuring relevance to real-world job market conditions. The system performance was evaluated using the Hit Rate Matrix. The results show that the Log-Likelihood Similarity method achieved a hit rate of 0.73, outperforming the Cosine Similarity method, which obtained a score of 0.51. This indicates that Log-Likelihood Similarity provides more accurate and relevant skill recommendations. Overall, the proposed system demonstrates the potential to assist job seekers in identifying relevant skills aligned with current market demands, thereby supporting better career decision-making in a competitive and dynamic job environment.]]>
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