CLASSIFICATION OF STUDENTS WITH ENGLISH LANGUAGE LEARNING DIFFICULTIES USING MACHINE LEARNING ALGORITHMS (CASE STUDY: STMIK KAPUTAMA)

Abstract

English language proficiency is essential in supporting students’ academic activities, especially in computer science education. Limited vocabulary mastery, reading comprehension, and coding performance often cause students to experience English language learning difficulties. This study aims to classify students with English learning difficulties using machine learning algorithms based on three variables: Vocabulary, Reading, and Coding. A quantitative approach is applied by processing student data obtained from tests and questionnaires. Machine learning classification models are evaluated using accuracy, precision, recall, and confusion matrix metrics. The results show that machine learning algorithms can effectively classify students with English learning difficulties and support data-driven educational decision making.