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首页> 外文会议>International Conference on Vocational Education and Training >Predicting high school graduates using Naive Bayes in State University Entrance Selections
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Predicting high school graduates using Naive Bayes in State University Entrance Selections

机译：在州立大学入学选择中使用朴素贝叶斯预测高中毕业生

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摘要

Competition to entering the best and major state universities that are suitable for student interest is a problem for some high school graduates. There is three entrance selection system to the state university, they call National Selection for Entering State University (SNMPTN), Joint Selection for Entering State University (SBMPTN), and University Self Selection. SNMPTN becomes a favorite admission for students because it does not require written exams. SNMPTN is carried out before the other admissions are conducted, therefore the SNMPTN admission had very high competition. Students will be proud when they are accepted in the favorite state university with the major of their interest. This research was conducted to predict the high school students majoring in science to entering the state university via SNMPTN admission at first choice using Naive Bayes and SMOTE methods. SMOTE is used to overcome the imbalance class of data. The results showed that using SMOTE can increase the accuracy of the Naive Bayes method. The performance of Naive Bayes showed an accuracy rate of 90.1%, a precision of 52,23%, and the recall of 51,66%. While, the performance of Naive Bayes combined with SMOTE method gave 5.39% improvement of the accuracy rate, 43.3% of the precision, and 43.83% of recall.
机译：进入适合学生兴趣的最好的和主要的州立大学的竞争对于某些高中毕业生来说是一个难题。州立大学有三种入学选择系统，分别称为“进入州立大学的国家选择（SNMPTN）”，“进入州立大学的联合选择”（SBMPTN）和“大学自选”。 SNMPTN成为学生的最爱入学，因为它不需要笔试。 SNMPTN准入是在进行其他准入之前进行的，因此SNMPTN准入竞争非常激烈。当学生以自己感兴趣的专业被最受欢迎的州立大学录取时，他们将为之自豪。进行这项研究的目的是预测使用Naive Bayes和SMOTE方法首选通过SNMPTN入学进入州立大学的理科专业的高中学生。 SMOTE用于克服数据的不平衡类别。结果表明，使用SMOTE可以提高朴素贝叶斯方法的准确性。朴素贝叶斯的性能显示出90.1％的准确率，52.23％的准确度和51.66％的召回率。同时，朴素贝叶斯与SMOTE方法相结合的性能使准确率提高了5.39％，精确度提高了43.3％，召回率提高了43.83％。

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