文章基本信息

标题：Clustering analysis of learning style on anggana high school student
本地全文：下载
作者：Siti Lailiyah ; Ekawati Yulsilviana ; Reza Andrea 等
期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：3
页码：1409-1416
DOI：10.12928/telkomnika.v17i3.9101
出版社：Universitas Ahmad Dahlan
摘要：The inability of students to absorb the knowledge conveyed by the teacher is’nt caused by the inability of understanding and by the teacher which isn’t able to teach too, but because of the mismatch of learning styles between students and teachers, so that students feel uncomfortable in learning to a particular teacher. It also happens in senior high school (SHS/SMAN) 1 Anggana, so it is necessary to do this research, to analyze cluster (group) of student learning style by applying data mining method that is k-Means and Fuzzy C-Means. The purpose was to know the effectiveness of this learning style cluster on the development of absorptive power and improving student achievement. The method used to cluster the learning style with data mining process starts from the data cleaning stage, data selection, data transformation, data mining, pattern evolution, and knowledge development.
其他摘要：The inability of students to absorb the knowledge conveyed by the teacher is’nt caused by the inability of understanding and by the teacher which isn’t able to teach too, but because of the mismatch of learning styles between students and teachers, so that students feel uncomfortable in learning to a particular teacher. It also happens in senior high school (SHS/SMAN) 1 Anggana, so it is necessary to do this research, to analyze cluster (group) of student learning style by applying data mining method that is k-Means and Fuzzy C-Means. The purpose was to know the effectiveness of this learning style cluster on the development of absorptive power and improving student achievement. The method used to cluster the learning style with data mining process starts from the data cleaning stage, data selection, data transformation, data mining, pattern evolution, and knowledge development.
关键词：fuzzy C-Means;K-Mean clustering;learning style