Optimizing K-Means Algorithm by Using Particle Swarm Optimization in Clustering for Students Learning Process

Inform

View Publication Info
 
 
Field Value
 
Title Optimizing K-Means Algorithm by Using Particle Swarm Optimization in Clustering for Students Learning Process
OPTIMIZING K-MEASN ALGORITHM USING PARTICLE SWARM OPTIMIZATION TO GROUP STUDENT LEARNING PROCESSES
 
Creator Hariyanto, Rudi
Sarwani, Mohammad Zoqi
 
Subject Learning process, Optimization Algorithm, PSO, K-Means, Clustering
Learning process,Optimasi, PSO, K-Means
 
Description In the implementation of learning, several factors affect the student learning process, including internal factors, external factors, and learning approach factors. For example, the physical and spiritual condition of students. Physiological aspects (body, eyes and ears and talents of students, student interests). External factors, for example, environmental conditions around students, family, teachers, community, friends) Thus, learning achievement is significant because educational institutions' success can be seen from how many students learning achievement. This research's first focus is to do student clustering based on their learning process using 11 parameters. Second, using the PSO algorithm to get maximum clustering results. The research data were obtained from vocational secondary education institutions in the city of Pasuruan. The data is obtained from the results of school reports and questionnaires as much as 100 student data. Data attributes include environmental features, social features, and related school features to group student data for learning data processing. From the classification results using the PSO method, the silhouette value is 0.97140754, very close. These results indicate that the PSO method can improve the K-Means clustering method's performance in the classification process of student learning interest. 
In the implementation of learning, there are several factors that affect the student learning process, including internal factors, external factors, and learning approach factors. Internal factors (factors within students), for example: the physical and spiritual condition of the student. Namely: physiological aspects (body, eyes and ears) and psychological aspects (student intelligence, student attitudes, student talents, student interests and student motivation). External factors (factors from outside students), for example: environmental conditions around students. Namely: social environment (family, teachers, community, friends) and non-social environment (home, school, equipment, nature). While the student learning approach factors, for example: The learning approach factor, namely the type of student effort which includes the strategies and methods used by students to carry out learning activities of subject matter, which consists of a high approach, medium approach and low approach. So the first focus of this research is to do student clustering based on their learning process using 11 parameters. Second, using the PSO algorithm to get maximum clustering results. The research data were obtained from vocational secondary education institutions in the city of Pasuruan. Where the data is data obtained from the results of school reports and questionnaires as much as 350 student data. Data attributes include environmental features, social features, and related school features to group student data for learning data processing. From the classification results using the PSO method, there are 0.97140754 silhouette values that are obtained because the distance between the data is very close. From these results indicate that the PSO method is able to improve the performance of the k-means clustering method in the classification process of student learning interest.
 
Publisher Universitas Dr. Soetomo
 
Contributor
Universitas Merdeka Pasuruan
 
Date 2021-01-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier https://ejournal.unitomo.ac.id/index.php/inform/article/view/3459
10.25139/inform.v6i1.3459
 
Source Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi; Vol 6, No 1 (2021); 65-68
2581-0367
2502-3470
 
Language eng
 
Relation https://ejournal.unitomo.ac.id/index.php/inform/article/view/3459/pdf
https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/3459/793
https://ejournal.unitomo.ac.id/index.php/inform/article/downloadSuppFile/3459/797
 
Rights Copyright (c) 2021 Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi
http://creativecommons.org/licenses/by-sa/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library