Classification of Learning Styles of Junior High School Students Using Random Forest & XGBoost Algorithm
DOI:
https://doi.org/10.30812/bite.v7i1.4913Keywords:
Classification, Learning Styles, Random Forest, XGBoostAbstract
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.
Downloads
References
[1] L. Rahmi and D. Yuswanti, “Meningkatan Motivasi Belajar Menggunakan Model Pembelajaran Picture and Picture pada Mata Pelajaran Ilmu Pengetahuan Alam,” JuDha_PGSD J. Dharma PGSD, vol. 1, no. 2, pp. 73–85, 2021, [Online]. Available: http://ejournal.undhari.ac.id/index.php/judha.
[2] Halawa and Mulyanti, “Faktor-Faktor Yang Mempengaruhi Peningkatan Kualitas Mutu Instansi Pendidikan Dan Pembelajaran,” Inspirasi Dunia J. Ris. Pendidik. dan Bhs., vol. 2, no. 2, pp. 57–64, 2023, doi: 10.58192/insdun.v2i2.757.
[3] F. Ikbal Andrian Prasetyo, Hanii Mustofa, Saman Supriadi, Ramadhan Hendra Prasetyo, Muhammad Yasri L, “Metode Decision Tree Dalam Pemilihan Gaya Belajar Pada Siswa Sekolah Dasar,” SAINTEK J. Ilm. Sains dan Teknol. Ind., vol. 5, no. 1, pp. 21–29, 2021, doi: 10.32524/saintek.v5i1.248.
[4] N. Awaliah, A. Herdiani, and G. A. A. Wisudiawan, “Deteksi Gaya Belajar berdasarkan Learning Behavior pada Learning Management System menggunakan Naïve Bayes,” e-Proceedings Eng., vol. 7, no. 1, pp. 2322–2342, 2020, [Online]. Available: https://libraryeproceeding.telkomuniversity.ac.id/index.php/engineering/article/view/11870/0.
[5] D. N. Sari, H. Oktavianto, and I. Saifudin3, “Penerapan Data Mining Untuk Klasifikasi Gaya Belajar Siswa Menggunakan Algoritma C4.5 Application,” J. Smart Teknol., vol. 3, no. 2, pp. 184–190, 2022.
[6] M. M. Aang Kisnu Darmawan, Kurdianto, Bakir, “Deteksi Gaya Belajar Siswa SMA pada Virtual Based Learning Environment(VBLE) dengan Decision Tree C4.5 dan Naive Bayes,” KLIK Kaji. Ilm. Inform. dan Komput., vol. 3, no. 5, pp. 532–544, 2023, [Online]. Available: https://djournals.com/klik.
[7] S. Ramadandi and J. Jahring, “Student Learning Style Classification Using Naïve Bayes Classifier Method,” J. Teknol. dan Inf., vol. 10, no. 2, pp. 170–179, 2020, doi: 10.34010/jati.v10i2.3096.
[8] V. A. Haq, “Menguji Validitas Dan Reliabilitas Pada Mata Pelajaran Al Qur’an Hadits Menggunakan Korelasi Produk Momenspearman Brown,” An-Nawa J. Stud. Islam, vol. 4, no. 1, pp. 11–24, 2022, doi: 10.37758/annawa.v4i1.419.
[9] A. Nugroho and D. Harini, “Teknik Random Forest untuk Meningkatan Akurasi Data Tidak Seimbang,” JSITIK J. Sist. Inf. dan Teknol. Inf. Komput., vol. 2, no. 2, pp. 128–140, 2024, doi: 10.53624/jsitik.v2i2.379.
[10] K. Davagdorj, V. H. Pham, N. Theera-Umpon, and K. H. Ryu, “Xgboost-based framework for smoking-induced noncommunicable disease prediction,” Int. J. Environ. Res. Public Health, vol. 17, no. 18, pp. 1–22, 2020, doi: 10.3390/ijerph17186513.
[11] A. Haris Prayoga and A. Voutama, “PENGEMBANGAN APLIKASI BANK ACCOUNT FRAUD DETECTION DENGAN MENGGUNAKAN ALGORITMA XGBOOST,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 3, pp. 2916–2922, May 2024, doi: 10.36040/jati.v8i3.9564.
[12] R. Harahap, M. Irpan, M. A. Dinata, L. Efrizoni, and Rahmaddeni, “Perbandingan Algoritma Random Forest Dan Xgboost Untuk Klasifikasi Penyakit Paru-Paru Berdasarkan Data Demografi Pasien,” J. Ilm. Betrik, vol. 15, no. 02, pp. 130–141, 2024.
[13] E. Y. Boateng, J. Otoo, and D. A. Abaye, “Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review,” J. Data Anal. Inf. Process., vol. 08, no. 04, pp. 341–357, 2020, doi: 10.4236/jdaip.2020.84020.
[14] M. Haris, “Analysis of The Application Of Hyperparameter Tuning in Machine Learning to Increase The Accuracy of Sales-Level Prediction,” J. Inform. Rekayasa Elektron., vol. 7, no. 1, pp. 98–106, 2024, [Online]. Available: http://e-journal.stmiklombok.ac.id/index.php/jireISSN.2620-6900.
[15] A. M. A. R. Kadek Adity Ananta Wisnu Wardana, “Analisis Perbandingan Algoritma XGBoost Dan Algoritma Random Forest Untuk Klasifikasi Data Kesehatan Mental,” Log. J. Ilmu Komput. dan Pendidik., vol. 2, no. 5, pp. 808–818, 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Christine Eirene, Dian Syafitri, Neny Sulistianingsih, Khasnur Hidjah, Hairani Hairani
This work is licensed under a Creative Commons Attribution 4.0 International License.
