<![CDATA[English proficiency plays a significant role in education, employment, and global communication. The TOEFL test is frequently used as an indicator to measure competence in listening, reading, speaking, and writing. This study aims to classify English proficiency levels based on TOEFL test data using the Decision Tree algorithm. The model was constructed by considering test scores and demographic attributes to obtain accurate predictions. Evaluation results showed that the model achieved an accuracy of 9.5%, precision of 9.1%, recall of 9.5%, and an F1-score of 9.1%. Although the model’s performance was relatively low, this approach can reveal certain patterns within the data that may serve as a foundation for developing more targeted English language learning strategies. This research contributes to the understanding of factors influencing TOEFL scores and provides recommendations for designing effective learning programs.]]>
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