Model Prediksi Kelulusan Mahasiswa Tepat Waktu dengan Metode Naïve Bayes

A. Armansyah, Rakhmat Kurniawan Ramli
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引用次数: 4

Abstract

There is a gap in the number of students in and out of graduating on this study program. The gap occurs due to the low graduation of students on time. Therefore, this study aims to design a model of student graduation predictions on time and not on time in finding solutions to that gap. The predictive model used in this study is Naïve Bayes. The data used in the form of 44 graduate data in 2020 is divided into two parts of the analysis stage with RapidMider, namely 38 graduates (training) and six data for testing. Our findings showed that the resulting research prediction model was excellent, with five data from six graduates matching the predictions in the first test, while one data was illegible. However, in the second test, six graduate data are exactly the same as modeling, whose accuracy shows a value of 100%.
以天真的Bayes方法,准时预测学生的毕业
参加这个学习项目的学生人数和毕业人数存在差距。这种差距是由于学生按时毕业率低造成的。因此,本研究旨在设计一个学生按时和不按时毕业预测模型,以寻找解决这一差距的方法。本研究使用的预测模型为Naïve Bayes。以2020年44个毕业生数据的形式使用的数据,用rapidmid将分析阶段分为两部分,即38个毕业生(培训)和6个用于测试的数据。我们的研究结果表明,最终的研究预测模型是优秀的,来自6名毕业生的5个数据与第一次测试的预测相匹配,而一个数据难以辨认。然而,在第二次测试中,6个毕业生数据与建模完全相同,其精度显示为100%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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