Abstract—Predicting student’s performance is one way that
can be conducted by university to monitor their student to
prevent student failed. Student final GPA is one parameter that
must be full fill by student to graduate from university and it
can be used to measure student’s performance. Educational
Data Mining is popular techniques to predict student’s
performance. This study tried to implement two popular data
mining clustering and classification analysis to predict student’s
performance. K-means algorithm is used since it is very popular
and easy to be implemented clustering algorithm. Linear
Regression and Support Vector Machine (SVM) then used to
predict the final GPA since the attributes used in this study is
numerical data. The clustered data and non-clustered data were
evaluated in the classification analysis and the MSE was
compared. The result showed that clustered data had smaller
RMSE and Linear Regression was better than SVM.
Index Terms—Student, performance prediction, educational
data mining.
The authors are with the Department of Industrial Engineering
Universitas Islam Indonesia, Jalan Kaliurang km. 14,5 Yogyakarta,
Indonesia (e-mail: annisa.uswatun@uii.ac.id, harwati@uii.ac.id).
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Cite: Annisa Uswatun Khasanah and Harwati, "Educational Data Mining Techniques Approach to Predict Student’s Performance," International Journal of Information and Education Technology vol. 9, no. 2, pp. 115-118, 2019.
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