Determining teacher certification eligibility is a crucial process in improving the quality of education. The C4.5 algorithm is a decision tree-based machine learning algorithm. This algorithm offers a systematic approach to data analysis and provides accurate results for decision-making. This study aims to develop a predictive model using the C4.5 algorithm to assess teacher certification eligibility based on relevant data such as teaching experience, education, and competency exam results. This study reveals that the C4.5 algorithm is capable of producing transparent decision rules and enabling clear interpretation of the results. This research is expected to make a significant contribution to supporting a more objective and efficient teacher certification policy.
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