<![CDATA[This study focuses on developing a web-based game recommendation system using a hybrid approach, combining K-Means Clustering and Content-Based Filtering to improve the accuracy and relevance of recommendations. The dataset was taken from the RAWG API, consisting of 1,000 games with key attributes such as name, Genre, platform, rating, and age category (ESRB). The research stages included Data Preparation, exploratory analysis, attribute transformation, application of K-Means for game segmentation, and similarity calculation using Cosine Similarity. The hybrid approach was carried out by filtering recommendations based on the same cluster. The results show that the integration of the two methods produces more relevant recommendations, with UMAP and t-SNE visualizations showing clear cluster separation. The system was implemented using Django and deployed on Google Cloud Platform, resulting in an efficient, adaptive, and real-time recommendation application.]]>
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