Classification of Students' Academic Performance Using Neural Network and C4.5 Model
DOI:
https://doi.org/10.59395/ijadis.v5i1.1311Keywords:
Classification, Students' academic, Neural networks , C4.5 algorithm , Data mining, Students' Academic Performance, Students Academic PerformanceAbstract
ducation involves deliberately creating an environment and learning process to empower students to fully utilize their academic and non-academic potential. It encompasses fostering spiritual qualities, religious understanding, self-discipline, cognitive abilities, and skills necessary for personal, societal, national, and state development. Madrasah Aliyah, in particular, emphasizes preparing participants for higher studies in areas of their interest, thereby showcasing their academic prowess. The evaluation of educational models like Neural Networks is crucial for ensuring their effectiveness in problem-solving. This involves testing and assessing the performance of the Neural Network model to ensure its accuracy and reliability. Similarly, the C4.5 method, based on condition data mining, is utilized to measure classification performance by assessing accuracy, precision, and recall. Research findings indicate that the neural network algorithm is more adept at accurately classifying students' academic abilities compared to the C4.5 algorithm. With an accuracy of 92.6% for the neural network algorithm and 80.6% for the C4.5 algorithm, it is evident that the former is more precise in determining the classification of students' academic abilities. This highlights the suitability of the neural network approach for classifying academic abilities in Madrasah Aliyah. Furthermore, the insights gained from this classification process can be extrapolated to benefit other madrasas.
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References
S. K. Slick, "Assessing versus assisting: The supervisor's roles in the complex dynamics of the student teaching triad," Teach Teach Educ, vol. 13, no. 7, pp. 713-726, Oct. 1997, doi: 10.1016/S0742-051X(97)00016-4. https://doi.org/10.1016/S0742-051X(97)00016-4
L. P. Khobragade and P. Mahadik, "Students academic failure prediction using data mining," International Journal of Advanced Research in Computer and Communication Engineering, vol. 4, 2015.
O. Taylan and B. Karagözouglu, "An adaptive neuro-fuzzy model for prediction of student's academic performance," Comput Ind Eng, vol. 57, no. 3, pp. 732-741, 2009. https://doi.org/10.1016/j.cie.2009.01.019
R. M. Tawafak, A. M. Romli, and M. J. Alsinani, "Student assessment feedback effectiveness model for enhancing teaching method and developing academic performance," International Journal of Information and Communication Technology Education, vol. 15, no. 3, pp. 75-88, 2019, doi: 10.4018/IJICTE.2019070106. https://doi.org/10.4018/IJICTE.2019070106
G. Zhang, B. Eddy Patuwo, and M. Y. Hu, "Forecasting with artificial neural networks: The state of the art," Int J Forecast, vol. 14, no. 1, pp. 35-62, 1998, doi: 10.1016/S0169-2070(97)00044-7. https://doi.org/10.1016/S0169-2070(97)00044-7
Z. Zhang, "Neural networks: further insights into error function, generalized weights and others," Ann Transl Med, vol. 4, no. 16, pp. 300-300, 2016, doi: 10.21037/atm.2016.05.37. https://doi.org/10.21037/atm.2016.05.37
G. P. Zhang, "Neural networks for classification: a survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 30, no. 4, pp. 451-462, 2000. https://doi.org/10.1109/5326.897072
E. Budiman, Haviluddin, N. Dengan, A. H. Kridalaksana, M. Wati, and Purnawansyah, "Performance of Decision Tree C4.5 Algorithm in Student Academic Evaluation," Lecture Notes in Electrical Engineering, vol. 488, pp. 380-389, 2018, doi: 10.1007/978-981-10-8276-4_36/COVER. https://doi.org/10.1007/978-981-10-8276-4_36
A. Cherfi, K. Nouira, and A. Ferchichi, "Very Fast C4.5 Decision Tree Algorithm," Applied Artificial Intelligence, vol. 32, no. 2, pp. 119-137, Apr. 2018, doi: 10.1080/08839514.2018.1447479. https://doi.org/10.1080/08839514.2018.1447479
S. B. Kotsiantis, "Decision trees: a recent overview," Artif Intell Rev, vol. 39, no. 4, pp. 261-283, 2013. https://doi.org/10.1007/s10462-011-9272-4
D. Saputra, W. Irmayani, D. Purwaningtias, J. Sidauruk, and B. Gurbuz, "A Comparative Analysis of C4.5 Classification Algorithm, Naïve Bayes and Support Vector Machine Based on Particle Swarm Optimization (PSO) for Heart Disease Prediction," International Journal of Advances in Data and Information Systems, vol. 2, no. 2, pp. 84-95, Oct. 2021, doi: 10.25008/IJADIS.V2I2.1221. https://doi.org/10.25008/ijadis.v2i2.1221
L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal, "Explaining explanations: An overview of interpretability of machine learning," Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018, pp. 80-89, Jul. 2018, doi: 10.1109/DSAA.2018.00018. https://doi.org/10.1109/DSAA.2018.00018
E. Sutoyo and A. Almaarif, "Educational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm," Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 1, pp. 95-101, 2020, doi: 10.29207/RESTI.V4I1.1502. https://doi.org/10.29207/resti.v4i1.1502
D. Y. T. Liu, K. Bartimote-Aufflick, A. Pardo, and A. J. Bridgeman, "Data-driven personalization of student learning support in higher education," Studies in Systems, Decision and Control, vol. 94, pp. 143-169, 2017, doi: 10.1007/978-3-319-52977-6_5/COVER. https://doi.org/10.1007/978-3-319-52977-6_5
R. Van Campenhout, B. Jerome, and B. G. Johnson, "Engaging in Student-Centered Educational Data Science Through Learning Engineering," pp. 3-40, 2023, doi: 10.1007/978-981-99-0026-8_1. https://doi.org/10.1007/978-981-99-0026-8_1
J. Han, M. Kamber, and J. Pei, Data Mining, Concepts and Techniques. 2012.
J. Han and M. Kamber, Data Mining, Southeast Asia Edition: Concepts and Techniques. Morgan kaufmann, 2006.
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