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Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier

Journal of Information Systems Engineering and Business Intelligence · Vol. 5 No. 1 (2019) · pp. 13–22

DOI
10.20473/jisebi.5.1.13-22
ISSN
2443-2555, 2598-6333
Published
2019-04-25
Publisher
Universitas Airlangga
Language
eng

Abstract

Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.Results: The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.

Keywords

Collaboration network of the authors

13 authors · 20 co-authorship links · 2 across different organisations

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  • Universitas Airlangga
  • Bandung Institute of Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Kridanto SurendroBandung Institute of Technology1
Eva HariyantiUniversitas Airlangga1
Delia Putri FardaniUniversitas Airlangga1
Eto WuryantoUniversitas Airlangga1
Endah PurwantiUniversitas Airlangga1
Bagus Fery YantoUniversitas Airlangga1
Rivanda PutraUniversitas Airlangga1
Ira PuspitasariUniversitas Airlangga1
Army JustitiaUniversitas Airlangga1
Nadhila Ramadhini AyuvindaUniversitas Airlangga1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Decision Support System for Classification of Early Childhood Diseases Using Principal Component Analysis and K-Nearest Neighbors Classifier</dc:title>
	<dc:creator>Dananjaya, Damar</dc:creator>
	<dc:creator>Werdiningsih, Indah</dc:creator>
	<dc:creator>Semiati, Rini</dc:creator>
	<dc:subject xml:lang="en-US">Toddler Diseases</dc:subject>
	<dc:subject xml:lang="en-US">Principal Component Analysis (PCA)</dc:subject>
	<dc:subject xml:lang="en-US">K-Nearest Neighbor Classifier</dc:subject>
	<dc:description xml:lang="en-US">Background: Data on early childhood disease collected in clinics has accumulated into big data. Those data can be used for classification of early childhood diseases to help medical staff in diagnosing diseases that attack early childhoods.Objective: This study aims to apply Principal Component Analysis (PCA) and K-Nearest Neighbor (K-NN) Classifier for the classification of early childhood diseases.Methods: Data analysis was performed using PCA to obtain variables that had a major influence on the classification of early childhood diseases. PCA was done by observing the correlation between variables and eliminating variables that have little influence on classification. Furthermore, data on early childhood disease was classified using the K-Nearest Neighbor Classifier method.Results:  The results of system evaluation using 150 test data indicated that the classification system by applying PCA and KNN Classifier had an accuracy value of 86%.Conclusion: PCA can be used to reduce the number of variables involved so that it can improve system performance in terms of efficiency. In addition, the application of PCA and KNN can also improve accuracy in the classification of early childhood diseases.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2019-04-25</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/9635</dc:identifier>
	<dc:identifier>10.20473/jisebi.5.1.13-22</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 5 No. 1 (2019): April; 13-22</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/9635/7386</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2019 Authors</dc:rights>
</oai_dc:dc>