PENERAPAN DATA MINING UNTUK PREDIKSI KELULUSAN MAHASISWA FAKULTAS TEKNIK INFORMATIKA UNIVERSITAS JABAL GHAFUR MENGGUNAKAN METODE K-NEAREST NEIGHBOR BERBASIS WEB
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引用次数: 0
Abstract
One of the instruments in campus accreditation, especially the faculty of informatics engineering at Jabal Ghafur University in order to get a good grade score is the satisfaction of graduate students getting decent jobs according to their fields, and is also a consideration during the study period for completing long lectures in a study program or faculty. The prediction of student graduation graduating on time or late does not only look at student data based on the highest scores as has been done in conventional grading systems so far. With advances in computer technology, especially in the field of data mining, it has brought many changes in the process of analyzing data patterns with data mining techniques, for example, in determining student graduation, a K-Nearest Neighbor method is used which processes data mining based on test data and sample data, especially on students. The results of the process for predicting student graduation are used sample data and training data. The sample data are students who are currently undergoing lectures and training data, namely students who have become alumni based on their graduation parameters. The final result obtained in the thesis research is that the system can input alternative data, criterion data, and process graduation predictions using the K-nearest neighbor method so that it can make a decision on whether the student who is being tested is "Passed" or "Graduated Late". The system can also display the K-nearest neighbor manual calculation flow and can display results reports.Key Words : Datamining, K-nearest Neighbor, Student Graduation Prediction, , PHPMySQL, Jabal Ghafur University
校园评审,特别是加富尔贾巴勒大学信息工程系的评审,为了获得一个好的成绩分数,其中一个手段就是研究生根据自己的专业找到体面工作的满意度,同时也是在学习期间完成学习课程或院系的长期授课的一个考虑因素。预测学生按时毕业或延迟毕业,并不像传统的评分系统那样只看最高分的学生数据。随着计算机技术的发展,特别是数据挖掘领域的发展,为利用数据挖掘技术分析数据模式的过程带来了许多变化,例如,在确定学生毕业时,使用了 K-近邻法,该方法根据测试数据和样本数据(特别是学生数据)进行数据挖掘。预测学生毕业的过程结果使用样本数据和训练数据。样本数据是正在接受授课的学生,而训练数据则是根据毕业参数确定的已成为校友的学生。论文研究的最终结果是,系统可以输入备选数据、标准数据,并使用 K 近邻法进行毕业预测处理,从而对被测试学生是 "通过 "还是 "延迟毕业 "做出判断。该系统还能显示 K 最近邻人工计算流程,并能显示结果报告:数据挖掘、K-近邻、学生毕业预测、PHPMySQL、贾巴勒加富尔大学