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There are many factors that are determinants of student performance, one of which is the consumption of alcohol by students, this can affect negative decision making which is a factor in the success of student performance. In this study classification techniques are used to predict students' interest in taking steps to continue their education to a higher level which is influenced by various factors, one of which is the level of alcohol consumption by students. By creating a model using the Naïve Bayes classification algorithm and Decision Tree which are tested on alcohol consumption data by students using Rapid Miner tools. Then the resulting model is compared to determine the best algorithm in identifying student performance. By using the Cross Validation Technique, statistics are obtained that show that the Decision Tree Algorithm has better performance when compared to Naïve Bayes. Decision Tree algorithm has an accuracy rate of 86.44% while Naïve Bayes only has an accuracy rate of 82.60%. And based on ROC statistics, it can be said that Naïve Bayes has quite poor performance with an Equal Error Rate (EER) of 65%, while Decision Tree has a lower EER level of 55%. That way the Decision Tree algorithm has a better performance in identifying student performance with the influence of various factors one of which is alcohol.

Komparasi Algoritma Klasifikasi untuk Prediksi Minat Sekolah Tinggi Pelajar pada Students Alcohol Consumption