<![CDATA[Recommendation systems have become essential components in various digital platforms to help users navigate through the increasingly diverse choices (Zakharia et al., 2024). In the digital era, the gaming industry has rapidly evolved with a multitude of genres and types of games available. This abundance of choices often makes it difficult for users to find games that match their preferences. Therefore, an accurate recommendation system is needed to facilitate users in selecting games relevant to their interests.This study aims to develop a game recommendation system using the Content-Based Filtering algorithm, where game features are analyzed to determine similarities with games favored by users. The Waterfall method is used as the development framework, covering stages of requirements analysis, system design, implementation, testing, and maintenance. The data used in this study was collected from the Kaggle website, which provides comprehensive information on user interactions with various games, including attributes such as genre, platform, and descriptions.The results of the study indicate that the Content-Based Filtering recommendation system is effective in providing recommendations that match user preferences. This research is expected to contribute to the development of more accurate recommendation systems and enhance user experience in exploring games that align with their interests.]]>
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