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CLASSIFICATION OF TODDLER’S NUTRITIONAL STATUS USING THE ROUGH SET ALGORITHM

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 17 No. 3 (2023) · pp. 1483–1494

DOI
10.30598/barekengvol17iss3pp1483-1494
ISSN
2615-3017, 1978-7227
Published
2023-09-30
Publisher
PATTIMURA UNIVERSITY
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The health and nutrition of children at the age of five are very important aspects in the children’s growth and development. An assessment of the nutritional status of toddlers that is commonly used is anthropometry. This study aims to obtain the decision rules used to classify toddlers into nutritional status groups using the rough set algorithm and determine the level of classification accuracy of the resulting decision rules. The index used in this study is the weight-for-age index. Attributes used in this study were the mother’s education level, mother’s level of knowledge, the status of exclusive breastfeeding, history of illness in the last month, and nutritional status of toddlers. The results of the analysis show that there are 21 decision rules. In this study, the resulting decision rules experience inconsistencies. The selection of decision rules that experience inconsistencies is based on each decision rule’s highest strength value. The rough set algorithm can be used for the classification process with an accuracy rate of 86.36%.

Collaboration network of the authors

18 authors · 35 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Politeknik TEDC Bandung
  • Andalas University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Riswan EfendiAffiliation unknown3
Nor Azah SamatAffiliation unknown3
Arrival Rince PutriAffiliation unknown2
Mega SusantiAffiliation unknown2
Frilianda WulandariAffiliation unknown2
Elvathna SyafwanPoliteknik TEDC Bandung1
Shandy TresnawatiPoliteknik TEDC Bandung1
Muthiah As SaidahAffiliation unknown1
Maiyastri MaiyastriAffiliation unknown1
Latifah ZahraAffiliation unknown1
Muhafzan MuhafzanAffiliation unknown1
Budi RudiantoAffiliation unknown1
Syafrizal SyAffiliation unknown1
Bala Muhammad MuhammadAffiliation unknown1

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://ojs3.unpatti.ac.id/index.php/barekeng/oai, first harvested 5 Nov 2026, last seen 5 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">CLASSIFICATION OF TODDLER’S NUTRITIONAL STATUS USING THE ROUGH SET ALGORITHM</dc:title>
	<dc:creator>Rahmi, Izzati</dc:creator>
	<dc:creator>Wulandari, Yana</dc:creator>
	<dc:creator>Yozza, Hazmira</dc:creator>
	<dc:creator>Syafwan, Mahdhivan</dc:creator>
	<dc:description xml:lang="en-US">The health and nutrition of children at the age of five are very important aspects in the children’s growth and development. An assessment of the nutritional status of toddlers that is commonly used is anthropometry. This study aims to obtain the decision rules used to classify toddlers into nutritional status groups using the rough set algorithm and determine the level of classification accuracy of the resulting decision rules. The index used in this study is the weight-for-age index. Attributes used in this study were the mother’s education level, mother’s level of knowledge, the status of exclusive breastfeeding, history of illness in the last month, and nutritional status of toddlers. The results of the analysis show that there are 21 decision rules. In this study, the resulting decision rules experience inconsistencies. The selection of decision rules that experience inconsistencies is based on each decision rule’s highest strength value. &amp;nbsp;The rough set algorithm can be used for the classification process with an accuracy rate of 86.36%. </dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2023-09-30</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://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8319</dc:identifier>
	<dc:identifier>10.30598/barekengvol17iss3pp1483-1494</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications; 1483-1494</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol17iss3year2023</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8319/6562</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Izzati Rahmi, Yana Wulandari, Hazmira Yozza, Mahdhivan Syafwan</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>