<![CDATA[This study aims to predict student satisfaction levels with teacher performance at vocational high schools (SMK) using data mining methods with the C4.5 algorithm. The research data was obtained through a teacher performance assessment questionnaire, which was processed using RapidMiner software to build a decision tree-based classification model. The model performance was evaluated using a Confusion Matrix with accuracy, precision, recall, and F1-score metrics, as well as Cohen's Kappa measurement to assess the suitability of the classification results. In addition, a cross-validation scheme was used to ensure the stability of the model's performance. The results showed that the C4.5 algorithm was able to classify student satisfaction levels with a very high degree of accuracy and a Cohen's Kappa value that indicated perfect classification consistency. The resulting model was able to clearly identify patterns of student satisfaction based on teacher performance indicators. This study shows that the C4.5 algorithm has the potential to be applied as a tool to support student satisfaction analysis in the field of education and can be further developed with more diverse datasets and comparison methods.]]>
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