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Clustering High School Students’ Career Interests Using K-Means with Multi-Metric Validation

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 20 No. 2 (2026) · pp. 175–186

DOI
10.22146/ijccs.117507
ISSN
2460-7258, 1978-1520
Published
2026-04-30
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Understanding students' career interests is essential for supporting effective career guidance programs in schools. However, identifying patterns of career interest among students is often challenging due to the diversity of motivational, cognitive, and planning-related factors. This study aims to analyze the segmentation of high school students' career interests using clustering techniques based on questionnaire data. This study uses the K-means algorithm run in conjunction with the Elbow Method to find the most appropriatenumber of clusters. The data preparation stages included cleaning the data, performing normalization using the Min-Max scaling method, and reducing the number of variables using principal component analysis (PCA) to facilitate visualization and initial analysis. In addition, cluster validity was evaluated using several internal validation indices, namely the silhouette score, Davies-Bouldin Index, and Calinski-Harabasz Index. The experimental results show that the data can be grouped into three clusters representing different levels of career interest characteristics among students. The identified clusters reveal variations in motivation, career planning clarity, and expectations for future careers. These findings provide useful insights for school counselors in designing targeted career guidance strategies.

Keywords

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Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Clustering High School Students’ Career Interests Using K-Means with Multi-Metric Validation</dc:title>
	<dc:creator>latifah, noor</dc:creator>
	<dc:creator>fatia, imelda annas</dc:creator>
	<dc:creator>adiyono, soni</dc:creator>
	<dc:subject xml:lang="en-US">data mining</dc:subject>
	<dc:subject xml:lang="en-US">Career Interest, Clustering Analysis, K-Means Algorithm, Elbow Method, Educational Data Mining</dc:subject>
	<dc:description xml:lang="en-US">Understanding students' career interests is essential for supporting effective career guidance programs in schools. However, identifying patterns of career interest among students is often challenging due to the diversity of motivational, cognitive, and planning-related factors. This study aims to analyze the segmentation of high school students' career interests using clustering techniques based on questionnaire data. This study uses the K-means algorithm run in conjunction with the Elbow Method to find the most appropriatenumber of clusters. The data preparation stages included cleaning the data, performing normalization using the Min-Max scaling method, and reducing the number of variables using principal component analysis (PCA) to facilitate visualization and initial analysis. In addition, cluster validity was evaluated using several internal validation indices, namely the silhouette score, Davies-Bouldin Index, and Calinski-Harabasz Index. The experimental results show that the data can be grouped into three clusters representing different levels of career interest characteristics among students. The identified clusters reveal variations in motivation, career planning clarity, and expectations for future careers. These findings provide useful insights for school counselors in designing targeted career guidance strategies. </dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-04-30</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/117507</dc:identifier>
	<dc:identifier>10.22146/ijccs.117507</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 20, No 2 (2026): April; 175-186</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/117507/43994</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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