Classification of Learning Styles of Junior High School Students Using Random Forest & XGBoost Algorithm - Christine Eirene; Dian Syafitri; Neny Sulistianingsih; Khasnur Hidjah; Hairani Hairani (Universitas Bumigora, 2025)
Background: Accurately identifying students' learning styles so that educators can adjust their teaching methods accordingly is a challenge in the field of education. However, the application of Machine Learning for learning style classification has not yet been implemented in schools in Mataram City. Objective: This study aims to classify the learning styles of students at Junior high school (SMP) Negeri 2 Mataram using Random Forest and XGBoost algorithms. Method: Data were collected through questionnaires completed by students in grades 7, 8, and 9. The results of data exploration (EDA) show data imbalance in the collected classes. Result: These results indicate that both algorithms performed well in classifying learning styles, with XGBoost showing slightly better performance. However, the accuracy obtained is not yet optimal, likely due to the limited dataset size. To address data imbalance, the SMOTE technique was applied. Initial evaluation showed that both XGBoost and Random Forest achieved an accuracy of 80%. After Hyperparameter Tuning, the accuracy of XGBoost increased to 84%, while Random Forest reached 82%. Conclusion: This study contributes to the application of Machine Learning in the education sector and highlights the need for further research to enhance model performance.