Classification of deep learning convolutional neural network feature extraction for student graduation prediction

Abu Salam, Junta Zeniarja


One indicator of a university’s educational quality is the proportion of enrolled students who actually graduate within four years. This proportion is typically fewer than the number of students that enroll in a given year. A low graduation rate can have a negative impact on both the university’s reputation and its accreditation because it indicates that fewer students are completing their degrees. Student activity, economic, and other issues all play a role in why some students are unable to complete their degrees on time. As a result, stakeholders need a model that can predict whether or not students will graduate on time as a means of evaluating and giving a basis for policy actions. This research proposes a model for converting textual data into an image format using a deep learning convolutional neural network (CNN), and then classifying the extracted features using a variety of machine learning classification algorithms like the decision tree, random forest, Naive Bayes, support vector machine (SVM), and k-nearest neighbor (K-NN). The classification model trained on feature extraction data had a 96.1% accuracy rate, while the classification model trained on the original data achieved a 71.2% accuracy rate.


Classification; Convolutional neural network; Deep learning; Graduation; Student

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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