Heny Pratiwi, Kusno Harianto
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引用次数: 5

摘要

本研究使用极限学习机和反向传播算法来找出哪种算法最适合了解学生的学习成绩。通过对学生的数据进行挖掘,得出规律,从而了解每年新生的特点。本研究的评估过程使用混淆矩阵引入正确识别的数据和未知数据。该算法在讲课开始时使用学生数据进行比较,作为早期发现有学术问题的学生的预期。变量为新生入学考试值、一年级IP值、性别、工作状态,输出变量为学业成绩分类的质量值。本研究结果表明,极限学习机算法的错误率为14.84%,低于反向传播算法的28.20%。从模型测试阶段来看,最准确的结果是Extreme Learning Machine算法,因为它具有最高的准确率和最低的错误率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perbandingan Algoritma ELM Dan Backpropagation Terhadap Prestasi Akademik Mahasiswa
Extreme Learning Machine and Backpropagation Algorithms are used in this study to find out which algorithm is most suitable for knowing student academic achievement. The data about students are explored to get a pattern so that the characteristics of new students can be known every year. The evaluation process of this study uses confusion matrix for the introduction of correctly recognized data and unknown data. Comparison of this algorithm uses student data at the beginning of the lecture as early detection of students who have problems with academics to be anticipated. The variables used are the value of the entrance examination for new students, the first grade IP value, Gender, and Working Status, while the output variable is the quality value as a classification of academic performance. The results of this study state that the Extreme Learning Machine algorithm has a 14.84% error rate lower than Backpropagation 28.20%. From the model testing stage, the most accurate result is the Extreme Learning Machine algorithm because it has the highest accuracy and the lowest error rate.
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