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Hybrid Particle Swarm Optimization and k-Means Clustering for Education Quality Mapping

by Muhammad Lintang Cahyo, Jatmiko Endro Suseno, Oky Dwi Nurhayati
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 180 - Number 42
Year of Publication: 2018
Authors: Muhammad Lintang Cahyo, Jatmiko Endro Suseno, Oky Dwi Nurhayati
10.5120/ijca2018917123

Muhammad Lintang Cahyo, Jatmiko Endro Suseno, Oky Dwi Nurhayati . Hybrid Particle Swarm Optimization and k-Means Clustering for Education Quality Mapping. International Journal of Computer Applications. 180, 42 ( May 2018), 39-45. DOI=10.5120/ijca2018917123

@article{ 10.5120/ijca2018917123,
author = { Muhammad Lintang Cahyo, Jatmiko Endro Suseno, Oky Dwi Nurhayati },
title = { Hybrid Particle Swarm Optimization and k-Means Clustering for Education Quality Mapping },
journal = { International Journal of Computer Applications },
issue_date = { May 2018 },
volume = { 180 },
number = { 42 },
month = { May },
year = { 2018 },
issn = { 0975-8887 },
pages = { 39-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume180/number42/29415-2018917123/ },
doi = { 10.5120/ijca2018917123 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:03:26.530758+05:30
%A Muhammad Lintang Cahyo
%A Jatmiko Endro Suseno
%A Oky Dwi Nurhayati
%T Hybrid Particle Swarm Optimization and k-Means Clustering for Education Quality Mapping
%J International Journal of Computer Applications
%@ 0975-8887
%V 180
%N 42
%P 39-45
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Quality mapping on education can be useful information to evaluate how well the quality of education is attained in each school. Solutions to overcome this problem required an information system for mapping the quality of education based on the five categories of quality achievement levels set by the government of Indonesia. This study was conducted using 200 school datasets in the city of Semarang. the study used the test parameters of input 20 particles, 40 particles, 80 particles and 160 particles. parameters were tested using PSO and PSO + k-Means methods. As a result, the use of the 20 particles parameter provides an optimal solution in grouping data at a central point.

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Index Terms

Computer Science
Information Sciences

Keywords

Information system clustering particle swarm optimization k-Means clustering

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