<![CDATA[YouTube is a video-sharing platform widely used by content creators, including those in the gaming category such as Mobile Legends. The intense competition among Mobile Legends content creators has made content performance analysis essential for developing data-driven content strategies. However, most previous studies have relied on manual data labeling, which is prone to subjectivity and lacks efficiency. Furthermore, high-accuracy classification models are often treated as black-box models, making it difficult to explain the factors influencing prediction outcomes. This study aims to develop a YouTube Mobile Legends content performance classification model by integrating K-Means, LightGBM, and Shapley Additive exPlanations (SHAP). The dataset consists of 3,052 videos collected using the YouTube Data API v3. The K-Means algorithm was employed to generate automatic labels, while LightGBM was used as the classification model and SHAP was applied to interpret the model's predictions. The results indicate that the optimal number of clusters was K=2, producing two performance classes: High Performance (72.44%) and Low Performance (27.56%). The dataset was subsequently divided into 80% for training and 20% for testing. On the testing dataset, the LightGBM model achieved an accuracy of 93.78%, precision of 93.87%, recall of 93.78%, and an F1-score of 93.81%. Furthermore, 10-fold cross-validation yielded a mean accuracy of 92.75% with a standard deviation of 0.0148, demonstrating the model's robustness and stability. SHAP analysis revealed that Subscriber Count was the most influential feature, followed by Duration, Video Age, and Title Length. These findings demonstrate that the integration of K-Means, LightGBM, and SHAP provides an effective approach for objectively, accurately, and transparently classifying the performance of Mobile Legends content on YouTube, while offering valuable insights to support content creators in developing data-driven content strategies.]]>
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