Comparison of the feature selection algorithm in educational data mining

Agung Triayudi, Iskandar Fitri


Student academic accomplishment is the foremost focus of every educational institution. In developing student achievement in educational institutions, the researchers finally created a new research area, namely Educational Data Mining (EDM). How the Feature Selection algorithm works is by removing unrelated data from educational datasets; therefore, this algorithm can improve the classification performance managed in EDM techniques. This research presents an analysis of the performance of the Feature Selection (FS) algorithm from the student dataset. The results received from other FS algorithms and classifiers will help other researchers to gain some best combination regarding Feature Selection algorithms and the classification. Selecting features that are relevant for student forecast models is a sensitive problem to stakeholders in education because they must make decisions based on the results of the prediction models. For the future, our paper seeks to play a decisive part while developing quality concerning education, as well as guiding different researchers in conducting educational interventions.


classification; decision; educational data mining; feature selection algorithm; student academic;

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