Prediksi Student Performance Pada Hasil Penilaian Proses Pembelajaran Online Mata Pelajaran Informatika Di SMA
DOI:
https://doi.org/10.32664/j-intech.v12i1.1259Kata Kunci:
informatika, random forest, student performance, SMA, XG boostingAbstrak
Di Endemi Corona, kita tidak sekedar kembali ke pola pendidikan offline namun sudah mengarah pada edukasi 5.0. Pola pembelajaran online, normal, blended sudah menjadi hal yang biasa. Penilaian pembelajaran Online membutuhkan prediksi student performance yang cepat dan tepat (akurasi tinggi). Penyebabnya adalah pertama, karena keterbatasan interaksi langsung. Kedua, pembelajaran normal biasanya ada penilaian proses belajar dan penilaian karakter untuk bisa memberikan penilaian akhir akurat itu sulit dapat dilaksanakan pada pembelajaran Online secara akurat. Ketiga, banyaknya data untuk diolah cepat dan tepat agar bisa dilaporkan kepada Institusi pendidikan dan pada keluarga peserta didik. Keempat, Informatika adalah pelajaran yang 80 % praktek dan 20 % teori sehingga instrument penilaian yang digunakan adalah 80 % instrument unjuk kerja (taksonomi bloom : C2, C3, C4, C5) dan 20 % instrument sejenis pilihan ganda (C1). Koreksi dan penilaian Informatika lebih membutuhkan banyak waktu karena 80 % tidak bisa dinilai secara otomatis. Penelitian ini bertujuan memprediksi student performance (Lulus (1)atau Intervensi (0)) pada hasil penilaian proses pembelajaran online mata pelajaran informatika di sma. Apabila hasil prediksi student performance menghasilkan Intervensi maka segera ditindaklanjuti dengan memberikan strategi intervensi supaya terjadi peningkatan student performance.Target hasil penelitian mencapai akurasi > 70 % terhadap dataset yang diolah. Penelitian ini menggunakan metode ensemble learning random Forest Classification dan XG Boosting classification. Hasil penelitian Prediksi Student Performance menggunakan XG Boost Classification menghasilkan akurasi lebih tinggi daripada RF Classification yang memiliki nilai akurasi rata-rata = 93 % sedangkan RF Classification memiliki hasil akurasi rata-rata = 92 %. Tujuan penelitian sudah tercapai karena hasil 2 metode yang digunakan sudah sesuai target yang diinginkan.
Referensi
Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., Bashir, M., & Khan, S. U. (2021). Predicting at-Risk Students at Different Percentages of Course Length for Early Intervention Using Machine Learning Models. IEEE Access, 9, 7519–7539. https://doi.org/10.1109/ACCESS.2021.3049446
Ahmad, S., Umirzakova, S., Mujtaba, G., Amin, M. S., & Whangbo, T. (2023). Education 5.0: Requirements, Enabling Technologies, and Future Directions. http://arxiv.org/abs/2307.15846
Ahmed, D. M., Abdulazeez, A. M., Zeebaree, D. Q., & Ahmed, F. Y. H. (2021). Predicting University’s Students Performance Based on Machine Learning Techniques. 2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021 - Proceedings, 276–281. https://doi.org/10.1109/I2CACIS52118.2021.9495862
Alwarthan, S., Aslam, N., & Khan, I. U. (2022). An Explainable Model for Identifying At-Risk Student at Higher Education. IEEE Access, 10, 107649–107668. https://doi.org/10.1109/ACCESS.2022.3211070
Bujang, S. D. A., Selamat, A., Ibrahim, R., Krejcar, O., Herrera-Viedma, E., Fujita, H., & Ghani, N. A. M. (2021). Multiclass Prediction Model for Student Grade Prediction Using Machine Learning. IEEE Access, 9, 95608–95621. https://doi.org/10.1109/ACCESS.2021.3093563
Ghorbani, R., & Ghousi, R. (2020). Comparing Different Resampling Methods in Predicting Students’ Performance Using Machine Learning Techniques. IEEE Access, 8, 67899–67911. https://doi.org/10.1109/ACCESS.2020.2986809
Kumar, V. U., Krishna, A., Neelakanteswara, P., & Basha, C. Z. (2020). Advanced Prediction of Performance of a Student in an University using Machine Learning Techniques. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), 121–126. https://doi.org/10.1109/ICESC48915.2020.9155557
Mienye, I. D., & Sun, Y. (2022). A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects. In IEEE Access (Vol. 10, pp. 99129–99149). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2022.3207287
Nabil, A., Seyam, M., & Abou-Elfetouh, A. (2021). Prediction of Students’ Academic Performance Based on Courses’ Grades Using Deep Neural Networks. IEEE Access, 9, 140731–140746. https://doi.org/10.1109/ACCESS.2021.3119596
Ogunleye, A., & Wang, Q. G. (2020). XGBoost Model for Chronic Kidney Disease Diagnosis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(6), 2131–2140. https://doi.org/10.1109/TCBB.2019.2911071
Perkasa, K. B. P. Y., & Eka Purwiantono, F. (2023). Sistem Rekomendasi Jurusan Menggunakan Algoritma Naïve Bayes Gaussian Berbasis Web. J-INTECH, 11(2), 361–370. https://doi.org/10.32664/j-intech.v11i2.1090
Sahlaoui, H., Alaoui, E. A. A., Nayyar, A., Agoujil, S., & Jaber, M. M. (2021). Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations. IEEE Access, 9, 152688–152703. https://doi.org/10.1109/ACCESS.2021.3124270
Saidani, O., Menzli, L. J., Ksibi, A., Alturki, N., & Alluhaidan, A. S. (2022). Predicting Student Employability Through the Internship Context Using Gradient Boosting Models. IEEE Access, 10, 46472–46489. https://doi.org/10.1109/ACCESS.2022.3170421
Shekar, B. H., & Dagnew, G. (2019). Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data. 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), 1–8. https://doi.org/10.1109/ICACCP.2019.8882943
Weerts, H. J. P., Mueller, A. C., & Vanschoren, J. (2020). Importance of Tuning Hyperparameters of Machine Learning Algorithms. http://arxiv.org/abs/2007.07588
Yao, J., Zheng, Y., & Jiang, H. (2021). An Ensemble Model for Fake Online Review Detection Based on Data Resampling, Feature Pruning, and Parameter Optimization. In IEEE Access (Vol. 9, pp. 16914–16927). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3051174
