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All Journal JOIV : International Journal on Informatics Visualization
Binti Muhammad Zahruddin, Nursyuhadah Alghazali
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Computer Science & IT

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Case Study: Using Data Mining to Predict Student Performance Based on Demographic Attributes Binti Muhammad Zahruddin, Nursyuhadah Alghazali; Kamarudin, Nur Diyana; Mat Jusoh, Ruzanna; Abdul Fataf, Nur Aisyah; Hidayat, Rahmat
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2454

Abstract

This study predicts student performance at Universiti Pertahanan Nasional Malaysia (UPNM) based on their socio-demographic profile; it also determines how a prediction algorithm can be used to classify the student data for the most significant demographic attributes. The analytical pattern in academic results per batch has been identified using demographic attributes and the student's grades to improve short-term and long-term learning and teaching plans. Understanding the likely outcome of the education process based on predictions can help UPNM lecturers enhance the achievements of the subsequent batch of students by modifying the factors contributing to the prior success. This study identifies and predicts student performance using data mining and classification techniques such as decision trees, neural networks, and k-nearest neighbors. This frequently adopted method comprises data selection and preparation, cleansing, incorporating previous knowledge datasets, and interpreting precise solutions. This study presents the simplified output from each data mining method to facilitate a better understanding of the result and determine the best data mining method. The results show that the critical attributes influencing student performance are gender, age, and student status. The Neural Networks method has the lowest Root of the Mean of the Square of Errors (RMSE) for accuracy measurement. In contrast, the decision tree method has the highest RMSE, which indicates that the decision tree method has a lower performance accuracy. Moreover, the correlation coefficient for the k-nearest neighbor has been recorded as less than one.
Co-Authors Abdul Fataf, Nur Aisyah Kamarudin, Nur Diyana Mat Jusoh, Ruzanna Rahmat Hidayat