Data mining began to be applied in various fields,
one of them on educational data. By exploring information or
knowledge in a data allows an institution to improve the learning
process and the quality of the institution. This research proposes
feature selection techniques in improving Student's Academic
Performance classification accuracy. The algorithm used is Naive
Bayes, Decision Tree, and Artificial Neural Network, which will
be applied to the features selection; wrapper and information
gain. The application of feature selection is intended to obtain a
higher accuracy value. When compared to the embedded method
in previous studies, the feature selection on this experiment has a
lower accuracy rate.
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83 Feature selection methods in improving accuracy of classifying students' academic pe.pdf
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Similarity Feature selection methods in improving accuracy of classifying students' academic performance.pdf