Improving the Performance of Classification via Clustering on the Students’ Academic Performance using Stacking Algorithm

Author:

Yamasari YuniORCID,Ahmad Rafif Aydin,Tjahyaningtijas Hapsari Peni AgustinORCID,Qoiriah AnitaORCID,Rochmawati NaimORCID,Prihanto Agus

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Fang, T., Huang, S., Zhou, Y., Zhang, H.: Multi-model stacking ensemble learning for student achievement prediction. In: Proceeding - International Symposium Parallel Architecture Algorithms Program. PAAP, vol. 2021, pp. 136–140 (2021). https://doi.org/10.1109/PAAP54281.2021.9720454

2. Burgos, C., Campanario, M.L., de la Peña, D., Lara, J.A., Lizcano, D., Martínez, M.A.: Data mining for modeling students’ performance: a tutoring action plan to prevent academic dropout. Comput. Electr. Eng. 66, 541–556 (2018). https://doi.org/10.1016/J.COMPELECENG.2017.03.005

3. Yamasari, Y., Qoiriah, A., Rochmawati, N., Yustanti, W., Tjahyaningtijas, H.P.A., Rusimamto, P.W.: Combining the unsupervised discretization method and the statistical machine learning on the students’ performance. In: 2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE), pp. 1–6, October 2020. https://doi.org/10.1109/ICVEE50212.2020.9243273

4. Juhaňák, L., Zounek, J., Rohlíková, L.: Using process mining to analyze students’ quiz-taking behavior patterns in a learning management system. Comput. Human Behav. (2017). https://doi.org/10.1016/J.CHB.2017.12.015

5. Harimurti, R., Yamasari, Y., Munoto, E., Asto, B.IG.P.: Predicting 10 student’s psychomotor domain on the vocational senior high school using linear regression. In: 2018 International Conference on Information and Communications Technology, ICOIACT 2018, vol. 2018-Janua, pp. 448–453, April 2018. https://doi.org/10.1109/ICOIACT.2018.8350768

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