Implementation of Algorithm C4.5 in Predicting Learning Readiness
DOI:
https://doi.org/10.63876/ijss.v1i1.2Keywords:
Algorithm C4.5, Learning readiness, PredictingAbstract
This paper discusses the implementation of the C4.5 algorithm in predicting learning readiness. Algorithm C4.5 is a machine learning technique that is often used to generate decision tree-based classification models. This study aims to develop a predictive model of learning readiness using the C4.5 algorithm. The data used in this study is secondary data obtained from the results of filling out the questionnaire by students. The stages in model development include data processing, making decision trees, and model evaluation. The results of the study show that the model developed using the C4.5 algorithm can predict learning readiness with fairly high accuracy. It is hoped that the results of this research can become a reference for the development of decision support systems in the education sector.
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D. Kuswandi et al., “Development Curriculum e-Learning: Student Engagement and Student Readiness Perspective,” in 2021 7th International Conference on Education and Technology (ICET), Malang, Indonesia, Sep. 2021, pp. 156–160. doi: 10.1109/ICET53279.2021.9575084.
F. T. Tehrani, “A New Decision Support System for Mechanical Ventilation,” in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, Aug. 2007, pp. 3569–3572. doi: 10.1109/IEMBS.2007.4353102.
A. S. Sunge, H. L. H. S. Warnar, Y. Heryadi, E. Abdurachman, B. Soewito, and F. L. Gaol, “Prediction Diabetes Mellitus Using Decision Tree Models,” in 2019 International Congress on Applied Information Technology (AIT), Yogyakarta, Indonesia, Nov. 2019, pp. 1–6. doi: 10.1109/AIT49014.2019.9144971.
Wella and V. U. Tjhin, “Exploring effective learning resources affecting student behavior on distance education,” in 2017 10th International Conference on Human System Interactions (HSI), Ulsan, South Korea, Jul. 2017, pp. 104–107. doi: 10.1109/HSI.2017.8005007.
T. Ruutmann and H. Kipper, “Rethinking effective teaching and learning for the design of efficient curriculum for technical teachers,” in 2012 15th International Conference on Interactive Collaborative Learning (ICL), Villach, Austria, Sep. 2012, pp. 1–9. doi: 10.1109/ICL.2012.6402030.
Y. Wang, “ePortfolios: A New Peer Assessment Technology in Educational Context,” in 2008 International Symposiums on Information Processing, Moscow, Russia, May 2008, pp. 360–363. doi: 10.1109/ISIP.2008.139.
S. A. Geertshuis, A. Bristol, M. E. A. Holmes, D. M. Clancy, and S. Sambrook, “Learning and business: supporting lifelong learning and the knowledge worker through the design of quality learning systems,” in Proceedings International Workshop on Advanced Learning Technologies. IWALT 2000. Advanced Learning Technology: Design and Development Issues, Palmerston North, New Zealand, 2000, pp. 186–187. doi: 10.1109/IWALT.2000.890604.
W. Baswardono, D. Kurniadi, A. Mulyani, and D. M. Arifin, “Comparative analysis of decision tree algorithms: Random forest and C4.5 for airlines customer satisfaction classification,” J. Phys. Conf. Ser., vol. 1402, no. 6, p. 066055, Dec. 2019, doi: 10.1088/1742-6596/1402/6/066055.
