基于k近邻的学业报告预测初中统考成绩

U. Pujianto, Mei Candra Kartikasari, Harits Ar Rosyid
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引用次数: 0

摘要

国家考试是印度尼西亚共和国政府为评价各级教育学习过程的表现而采用的一种机制。这项研究认为,2016年至2018年发生的初中学生全国考试成绩下降是一个需要解决的问题。应用于成绩单分数的k-最近邻法用于预测学生在国家考试中的成绩,从四个科目进行测试。包含307个实例的数据集是从印度尼西亚玛琅的一所国立初中获得的主要数据。性能比较研究中使用了两种场景,其中一种涉及SMOTE重新采样。结果表明,以k近邻为分类器并结合SMOTE预处理的场景产生的性能最好。国家考试预测中表现最好的是英语,准确率为81.16%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Junior High School National Exam Results Based on Academic Report Using K-Nearest Neighbor
The National Examination is a mechanism adopted by the Government of the Republic of Indonesia to evaluate the performance of the learning process at every level of education. This study sees the decline in national exam scores for junior high school students that occurred during 2016 to 2018 as a problem that needs to be resolved. The k-Nearest Neighbor method which is applied to the report card scores is used to predict the achievement of student performance in the national exam from the four subjects tested. The dataset containing 307 instances resulted from the acquisition of primary data from a state Junior High School in Malang, Indonesia. Two scenarios, one of which involved SMOTE resampling, were used in the performance comparison study. The results showed that the best performance was generated by a scenario involving k-Nearest Neighbor as the classifier, combined with SMOTE preprocessing. The best performance of national exam predictions can be seen in English, with an accuracy of 81.16%.
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