Student academic performance analysis using fuzzy C-means clustering

被引：0

作者：

Rosadil, R. ^{[1
]}

Akamal ^{[1
]}

Sudrajat, R. ^{[1
]}

Kharismawan, B. ^{[2
]}

Hambali, Y. A. ^{[3
]}

机构：

[1] Padjadjaran State Univ, Dept Comp Sci, Sumedang 45363, Indonesia

[2] Padjadjaran State Univ, Dept Math, Sumedang 45363, Indonesia

[3] Inst Technol Bandung, STEL, Bandung 40132, Indonesia

来源：

INDONESIAN OPERATIONS RESEARCH ASSOCIATION (IORA) - INTERNATIONAL CONFERENCE ON OPERATIONS RESEARCH 2016 | 2017年 / 166卷

关键词：

D O I：

10.1088/1757-899X/166/1/012036

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

Grade Point Average (GPA) is commonly used as an indicator of academic performance. Academic performance evaluations is a basic way to evaluate the progression of student performance, when evaluating student's academic performance, there are occasion where the student data is grouped especially when the amounts of data is large. Thus, the pattern of data relationship within and among groups can be revealed. Grouping data can be done by using clustering method, where one of the methods is the Fuzzy C-Means algorithm. Furthermore, this algorithm is then applied to a set of student data form the Faculty of Mathematics and Natural Sciences, Padjadjaran University.

引用

页数：6

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