Implementation of the C4.5 Algorithm in Predicting the Interest of Prospective Students in Choosing Higher Education
Author's Country: Indonesia
DOI:
https://doi.org/10.36805/bit-cs.v6i1.7697Keywords:
Prediction, Classification, Data Mining, C4.5 Algorithm, Decision TreeAbstract
Data governance involves setting internal standards for how data is collected, stored, processed and deleted. In the context of the oil industry, data governance can intervene at the level of oil exploration and production to manipulate data in particular. In our contribution, we explain how Fuzzy c - means based machine learning can be used for oil data governance. This deep artificial intelligence concept, which we will use in addition to fuzzy logic, by applying Fuzzy c - means for good training can enable the decision-maker a better governance policy.
Downloads
References
Alwarthan, S. A., Aslam, N., & Khan, I. U. (2022). Predicting Student Academic Performance at Higher Education Using Data Mining: A Systematic Review. In Applied Computational Intelligence and Soft Computing (Vol. 2022). Hindawi Limited. https://doi.org/10.1155/2022/8924028
Berrar, D. (2019). Bayes’ Theorem and Naive Bayes Classifier. In S. Ranganathan, M. Gribskov, K. Nakai, & C. Schönbach (Eds.), Encyclopedia of Bioinformatics and Computational Biology (pp. 403–412). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-809633-8.20473-1
Feng, L. (2021). Research on Higher Education Evaluation and Decision-Making Based on Data Mining. Scientific Programming, 2021. https://doi.org/10.1155/2021/6195067
Galit Shmueli, P. C. B. I. Y. N. R. P. K. C. L. Jr. (2018). DATA MINING FOR BUSINESS ANALYTICS.
Gerhana, Y. A., Fallah, I., Zulfikar, W. B., Maylawati, D. S., & Ramdhani, M. A. (2019). Comparison of naive Bayes classifier and C4.5 algorithms in predicting student study period. Journal of Physics: Conference Series, 1280(2). https://doi.org/10.1088/1742-6596/1280/2/022022
Hu, J., & Li, H. (2021). Composition and Optimization of Higher Education Management System Based on Data Mining Technology. Scientific Programming, 2021. https://doi.org/10.1155/2021/5631685
Masters, T. (2018). Data Mining Algorithms in C++. In Data Mining Algorithms in C++. Apress. https://doi.org/10.1007/978-1-4842-3315-3
Naga, J. F., & Tinam-Isan, M. A. C. (2024). EXPLORING THE INFLUENCE OF PERSONALITY TRAITS ON STUDENTS’ INFORMATION SECURITY RISK-TAKING BEHAVIORS: A BFI ASSESSMENT. Procedia Computer Science, 234, 527–536. https://doi.org/10.1016/j.procs.2024.03.036
Ngoc, P. V., Ngoc, C. V. T., Ngoc, T. V. T., & Duy, D. N. (2019). A C4.5 algorithm for english emotional classification. Evolving Systems, 10(3), 425–451. https://doi.org/10.1007/s12530-017-9180-1
Nurmalitasari, Awang Long, Z., & Faizuddin Mohd Noor, M. (2023). Factors Influencing Dropout Students in Higher Education. Education Research International, 2023. https://doi.org/10.1155/2023/7704142
Parteek Bhatia. (2019). Data Mining and Data Warehousing.
Wang, C. (2021). Analysis of Students’ Behavior in English Online Education Based on Data Mining. Mobile Information Systems, 2021. https://doi.org/10.1155/2021/1856690
Wang, J. (2022). Application of C4.5 Decision Tree Algorithm for Evaluating the College Music Education. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/7442352
Yang, X., & Ge, J. (2022). Predicting Student Learning Effectiveness in Higher Education Based on Big Data Analysis. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/8409780
Ye, H., & Li, C. (2022). Engineering Education Understanding Expert Decision System Research and Application. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/9662301

Downloads
Published
Issue
Section
License
This work is licensed under a Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional.