<![CDATA[The development of digital marketing has led companies to increasingly rely on influencers as an effective promotional medium. However, selecting the right influencer remains a challenge because the process is often conducted manually and only considers basic attributes such as the number of followers and popularity level, this study focuses on developing a web-based influencer recommendation system that can provide more relevant and faster recommendations. The research was conducted on Ralya Management, an agency managing influencers in Indonesia that requires a system capable of improving the efficiency of influencer selection for various marketing campaigns. To achieve its objectives, this study adopts two main approaches. First, the Content-Based Filtering method is applied to match influencer content characteristics including category, keywords, engagement rate, and domicile with user preferences, resulting in more targeted recommendations. Second, the system development process utilizes the Agile Development method with the Scrum framework, enabling iterative, flexible, and adaptive development in response to influencer data updates.. The results show that the system is capable of producing more accurate influencer recommendations compared to the previous manual method. Additionally,. Overall, this system contributes significantly to enhancing the effectiveness of influencer marketing strategies, particularly for Ralya Management as the research subject.]]>
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