Implementation of XGBoost for Classifying Student Interest in Informatics at SMP Negeri 30 Bengkulu Selatan
DOI:
https://doi.org/10.37676/jki.v5i3.1859Keywords:
Explainable Artificial Intelligence, Student Learning Interest, XGBoostAbstract
Student interest in Informatics is still commonly identified through teacher observation, creating a need for a more objective data-driven approach. This study develops an XGBoost classification model and explains the factors underlying its decisions using SHAP. A quantitative data-analytics design followed the CRISP-DM stages using 27 student records and seven features: assignment scores, daily tests, practical scores, midterm scores, final scores, attendance, and attitude. The dataset was split into 80% training and 20% testing data. On six held-out cases, the model correctly classified two students as Interested and four as Not Interested, producing 100% Accuracy, Precision, Recall, and F1-Score, with an AUC of 1.00. SHAP analysis identified daily-test scores as the strongest contributor (1.1179), followed by assignment, final-exam, practical, and midterm scores. These findings indicate that XGBoost combined with SHAP can support measurable and transparent identification of student interest, although the limited dataset requires caution when generalizing the performance.
References
1. Alfarizi, M. R. S., Al-farish, M. Z., Taufiqurrahman, M., Ardiansah, G., & Elgar, M. (2023). Penggunaan Python sebagai bahasa pemrograman untuk machine learning dan deep learning. Karya Ilmiah Mahasiswa Bertauhid (KARIMAH TAUHID), 2(1), 1–6.
2. Alfiko, F. (2025). Analisis kesulitan belajar siswa pada mata pelajaran Informatika kelas X Perhotelan 2 SMK N 7 Kota Bengkulu. Computer and Informatics Education Review-CIER, 2025(1), 49–56.
3. Ekawati, R. R., Atina, V., & Maulidar, J. (2025). Prediksi ketuntasan belajar siswa menggunakan Naive Bayes dengan integrasi data akademik. Jurnal Pendidikan dan Teknologi Indonesia (JPTI), 5(4), 1161–1173.
4. Florent, A., Rizal, M., & Oka, S. (2025). Prediksi kinerja siswa SMP berdasarkan data akademik dan perilaku menggunakan machine learning. Teknomatika: Jurnal Informatika dan Komputer, 18(2), 67–76.
5. Istiwana Putri Agnes. (2026). Pendekatan explainable machine learning untuk analisis faktor drop out mahasiswa menggunakan XGBoost. Machine Learning and Knowledge Extraction, 5(1), 169–170. https://doi.org/10.3390/make5010010
6. Kasliono, K., Suharmono, E., Povi, P., Meriani, R., & Candraningrum, N. (2023). Analisis regresi dan korelasi untuk proyeksi produksi minyak bumi dan gas alam Indonesia menggunakan bahasa pemrograman Python. Jurnal Teknologi Informatika dan Komputer, 9(2), 1297–1313. https://doi.org/10.37012/jtik.v9i2.1756
7. Marcydiaz, A. H., Fitriya, F. N., Hutagaol, A. S., Trisnawarman, D., & Beng, J. T. (2024). Perancangan datamart nilai akademik siswa pada SMA Z Bekasi. INTECOMS: Journal of Information Technology and Computer Science, 7(6), 2040–2047. https://doi.org/10.31539/intecoms.v7i6.12916
8. Massahiro, A., Instituto, S., Tecnológicas, D. P., Paulo, D. S., Univer-, F., Paulo, S., Cordeiro, R., & Paulo, S. (2024). The evolution of CRISP-DM for data science: Methods, processes and frameworks. https://doi.org/10.5753/reviews.2024.3757
9. Pangesthi, G., & Triyani. (2022). Buku interaktif informatika: Untuk SMP/MTs. Intan Pariwara.
10. Putri Eka Adek. (2024). Analisis faktor-faktor kesulitan belajar siswa pada mata pelajaran geografi materi bumi sebagai ruang kehidupan di kelas X SMA Negeri 4 Pariaman. Jurnal Pendidikan Tambusai, 8(1), 11724–11730. https://doi.org/10.31004/jptam.v8i1.14151
11. Putu, N., Yuniarti, M., Ayu, G., & Sukma, P. (2022). Efektivitas media pembelajaran terhadap minat belajar siswa Videoscribe Connected. 5(1), 160–171.
12. Safii, M., Husain, Kirana, I. O., Leana, S. A., & Gultom, Y. I. (2025). Model prediksi penjadwalan produksi energi terbarukan dengan algoritma XGBoost dan analisis interpretatif menggunakan SHAP. Jurnal Sistem Informasi Triguna Dharma (JURSI TGD), 4(4), 794–801. https://doi.org/10.53513/jursi.v4i4.11443
13. Wardhani, K. D. K., & Akbar, M. (2022). Diabetes risk prediction using Extreme Gradient Boosting (XGBoost). Jurnal Online Informatika, 7(2), 244–250. https://doi.org/10.15575/join.v7i2.970
14. Wibowo, E. A., & Aryanti, R. (2025). Penerapan metode clustering K-Means menggunakan RapidMiner untuk klasifikasi prestasi siswa di sekolah swasta. Journal of Information Technology and Informatics Engineering, 1(1), 20–24.
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