<![CDATA[The student learning level placement process at New Concept English Education Centre is still conducted manually through interviews and teacher observations, which may lead to subjectivity in class placement. This study aims to implement the Random Forest algorithm to classify the learning levels of new students and integrate it into a web-based decision support system. The study employed 1,522 student records consisting of age, school grade, placement test scores, and learning level labels. The research stages included data preprocessing, categorical data encoding, an 80:20 train–test split, Random Forest model training, evaluation using a confusion matrix and classification report, and implementation of the trained model into a web-based application. The evaluation results indicate that the Random Forest model achieved an accuracy of 85.57% in classifying student learning levels. The developed system supports a more consistent, objective, and efficient learning level placement process, thereby assisting teachers in determining appropriate class placement for new students.]]>
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