Prediction Of The Number Of Course Participants Using Random Forest Regression Algorithm
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Abstract
Classroom management is the process of using resources effectively to achieve goals. Planning the schedule is not easy because sometimes, after a schedule designed and when the schedule is published and used, there are problems where the class division of a course is not right on target. This study used the random forest regression method to predict the number of class participants. Data pattern affects the accuracy of calculating the predicted value. The best RMSE and MAE results in the Matematika Dasar Course are 6,51 for RMSE and 2,12 for MAE. At the same time, the prediction of course participant number is 73,18.
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How to Cite
Septiriana, R., Perwitasari, A. and Tursina (2022) “Prediction Of The Number Of Course Participants Using Random Forest Regression Algorithm ”, Jurnal Mantik, 6(3), pp. 3393-3399. doi: 10.35335/mantik.v6i3.3175.
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Astuti, A. S. T. U. T. I. (2019). Manajemen Kelas yang Efektif. Adaara: Jurnal Manajemen Pendidikan Islam, 9(2), 892-907.
Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
Chai, R., & Gunawan, D. (2022). Optimizing CNN Hyperparameters for Blastocyst Quality Assessment in Small Datasets. IEEE Access, 10, 88621-88631.
Herdianto. (2013). Prediksi Kerusakan Motor Induksi Menggunakan Metode. Jaringan Saraf Tiruan Backpropagation, Tesis, Universitas Sumatera Utara.
Indahyanti, U., Azizah, N. L., & Setiawan, H. (2022). Educational Data Mining on Student Academic Performance Prediction: A Survey. Procedia of Social Sciences and Humanities, 3, 1442-1447.
Khaleghi, S., Firouz, Y., Van Mierlo, J., & Van Den Bossche, P. (2019). Developing a real-time data-driven battery health diagnosis method, using time and frequency domain condition indicators. Applied Energy, 255, 113813.
Lahouar, A., & Slama, J. B. H. (2017). Hour-ahead wind power forecast based on random forests. Renewable energy, 109, 529-541.
Li, Y., Zou, C., Berecibar, M., Nanini-Maury, E., Chan, J. C. W., Van den Bossche, P., ... & Omar, N. (2018). Random forest regression for online capacity estimation of lithium-ion batteries. Applied energy, 232, 197-210.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R news, 2(3), 18-22.
Na, X. D., Zang, S. Y., Wu, C. S., & Li, W. L. (2015). Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques. Environmental monitoring and assessment, 187(11), 1-17.
Rianto, H & Wahono, R. S.. (2015). Resampling Logistic Regression untuk Penanganan Ketidakseimbangan Class pada Prediksi Cacat Software. Journal of Software Engineering, 1(1).
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS journal of photogrammetry and remote sensing, 67, 93-104.
Saputra, T. A. N., Arizona, K. I., Andrian, M. R., Kurniadi, F. I., & Juarto, B. (2022, August). Random Forest in Detecting Hepatitis C. In 2022 9th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE) (pp. 299-302). IEEE.
Shu, X., Chen, Z., Shen, J., Guo, F., Zhang, Y., & Liu, Y. (2022). State of Charge Estimation for Lithium-ion Battery Based on Hybrid Compensation Modeling and Adaptive H-Infinity Filter. IEEE Transactions on Transportation Electrification.
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