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Identification of Potential Student Academic Ability using Comparison Algorithm K-Means and Farthest First

by Athanasia O. P. Dewi, Wiranto H. Utomo, Sri Yulianto J. P.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 17
Year of Publication: 2013
Authors: Athanasia O. P. Dewi, Wiranto H. Utomo, Sri Yulianto J. P.
10.5120/10558-5631

Athanasia O. P. Dewi, Wiranto H. Utomo, Sri Yulianto J. P. . Identification of Potential Student Academic Ability using Comparison Algorithm K-Means and Farthest First. International Journal of Computer Applications. 63, 17 ( February 2013), 18-26. DOI=10.5120/10558-5631

@article{ 10.5120/10558-5631,
author = { Athanasia O. P. Dewi, Wiranto H. Utomo, Sri Yulianto J. P. },
title = { Identification of Potential Student Academic Ability using Comparison Algorithm K-Means and Farthest First },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 17 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 18-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number17/10558-5631/ },
doi = { 10.5120/10558-5631 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:36.473199+05:30
%A Athanasia O. P. Dewi
%A Wiranto H. Utomo
%A Sri Yulianto J. P.
%T Identification of Potential Student Academic Ability using Comparison Algorithm K-Means and Farthest First
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 17
%P 18-26
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The paper is tell about how to measure the potential of students' academic skills by using the parameter values and the area by using clustering analysis comparing two algorithms, algorithm K-Means and Farthest First algorithm. The data used in this paper is the student data of private universities in Indonesia. Tools that used in this study is Weka data mining application. From the results observed, found that the origin of high school affect the values obtained during the lectures and the more the number of clusters desired, the more also the time required to perform the data clustering.

References
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  11. Sunita, B. A. , Lobo, L. M. R. J. 2012. A comparative Study for Selecting the Best Unsupervised Learning Algorithm in E-Learning System.
  12. Velmurugan, T. , Santhanam, T. 2010. Performance Evaluation of K-Means and Fuzzy C-Means Clustering Algorithms for Statistical Distributions of Input Data Points.
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Index Terms

Computer Science
Information Sciences

Keywords

Clustering algorithms K-Means Farthest First

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