<![CDATA[Early detection in providing recommendations for student counseling is very important, therefore you can assess the student's potential, beliefs, and attitude as early as possible. The problem that arises in this case is how to detect a student early so that he or she needs counseling assistance or not so that it can be identified early to minimize the risk of further psychological conditions. This article proposes a data mining model using a decision tree to classify counseling recommendations for students. In addition, to improve the resulting accuracy performance, a feature selection method is proposed using forward selection and genetic algorithms. The stages of the research were carried out by pre-processing the data, implementing algorithms, validating data, and optimizing the model. The experimental results show that the best level of accuracy using the decision tree model is 95.64%. It increases to 96.91% after optimization using the genetic algorithm.]]>
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