Classification of students' academic performance using Neural Network and C4.5 model

Sulika, Sulika, Kusumawati, Ririen ORCID: https://orcid.org/0000-0001-6090-7219 and Arif, Yunifa Miftachul ORCID: https://orcid.org/0000-0002-2183-0762 (2024) Classification of students' academic performance using Neural Network and C4.5 model. International Journal of Advances in Data and Information Systems, 5 (1). pp. 29-38. ISSN 27213056

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Abstract

Education 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.

Item Type:	Journal Article
Keywords:	Classification; Students' academic; Neural networks; C4.5 algorithm; Data mining; Students' Academic Performance; Students Academic Performance
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Yunifa Miftachul Arif
Date Deposited:	30 Apr 2024 14:36

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