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Comparison of single, complete, and average linkage algorithms in clustering instant noodle products based on nutritional content

Unnes Journal of Mathematics · Vol. 15 No. 1 (2026) · hlm. 25–33

DOI
10.15294/ujm.v15i1.25549
ISSN
2460-5859, 2252-6943
Terbit
2026-10-21
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

This study aims to perform clustering on instant noodle products based on their nutritional content to assist consumers in making informed choices according to their dietary needs. The method employed is Hierarchical Agglomerative Clustering (HAC), with a comparison of three linkage algorithms: single linkage, complete linkage, and average linkage. The implementation was carried out using the Python programming language on the Google Colab platform. The results indicate that the complete linkage algorithm is the most effective, achieving a silhouette score of 0.4445 and producing two optimal clusters consisting of 98 and 40 products, respectively. These findings suggest that nutritional content–based clustering can serve as a useful reference for providing more informed and responsible instant noodle consumption recommendations.

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  • State University of Semarang
  • Yogyakarta State University
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Rekan penulisOrganisasiArtikel bersama
Rochmad RochmadState University of Semarang6
Ary Woro KurniasihState University of Semarang5
Masrukan MasrukanState University of Semarang3
Mashuri MashuriState University of Semarang3
Kholifatu Ulil AzmiState University of Semarang2
Mulyono MulyonoState University of Semarang2
Muhammad KharisState University of Semarang2
Muhammad Zuhair ZahidState University of Semarang2
Abid AlauddinState University of Semarang1
Muhammad Hidzam FattahState University of Semarang1
Alldila Marizka Putri LesmonoState University of Semarang1
Azahra AldebbyState University of Semarang1
Racel Eris AbeleaState University of Semarang1
Adinda Anggun CahyaniState University of Semarang1
Violla MaharaniState University of Semarang1
Abil Kurnia Dwi PutraState University of Semarang1
Almira Keisha YunintaState University of Semarang1
Francesco Gusli Pratama -State University of Semarang1
Rembulan Permata OctaliaState University of Semarang1
St. Budi WaluyaAfiliasi tidak diketahui1
Scolastika MarianiState University of Semarang1
Annisa Salma ZakiyyahState University of Semarang1
Puput RelitasariState University of Semarang1
MashuriState University of Semarang1
Chitra Ayu NevtianingsiState University of Semarang1
Zuyun KhoiriyahState University of Semarang1
Rani AnggrainiState University of Semarang1
Sugiman SugimanYogyakarta State University1
Ahmad Ijlal AbdikaState University of Semarang1
Meli SeptianaState University of Semarang1
Ratih KusumawatiState University of Semarang1
Wuryanto WuryantoState University of Semarang1
Hardi SuyitnoState University of Semarang1
Ula Himatul AliyahState University of Semarang1
Puji HandayaniAfiliasi tidak diketahui1
Ylyas SopyyevState University of Semarang1
Endang Retno WinartiState University of Semarang1
Rina Nurul FathiyaState University of Semarang1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/ujm/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en">Comparison of single, complete, and average linkage algorithms in clustering instant noodle products based on nutritional content</dc:title>
	<dc:creator xml:lang="en">Sari, Fitri Novita</dc:creator>
	<dc:creator xml:lang="en">Arief Agoestanto</dc:creator>
	<dc:subject xml:lang="en">Instant Noodles, Hierarchical Agglomerative Clustering, Nutritional Content, Clustering, Linkage Algorithm</dc:subject>
	<dc:description xml:lang="en">This study aims to perform clustering on instant noodle products based on their nutritional content to assist consumers in making informed choices according to their dietary needs. The method employed is Hierarchical Agglomerative Clustering (HAC), with a comparison of three linkage algorithms: single linkage, complete linkage, and average linkage. The implementation was carried out using the Python programming language on the Google Colab platform. The results indicate that the complete linkage algorithm is the most effective, achieving a silhouette score of 0.4445 and producing two optimal clusters consisting of 98 and 40 products, respectively. These findings suggest that nutritional content–based clustering can serve as a useful reference for providing more informed and responsible instant noodle consumption recommendations.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-10-21</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/ujm/article/view/25549</dc:identifier>
	<dc:identifier>10.15294/ujm.v15i1.25549</dc:identifier>
	<dc:source xml:lang="en">Unnes Journal of Mathematics; Vol. 15 No. 1 (2026): Unnes Journal of Mathematics Volume 1, 2026; 25-33</dc:source>
	<dc:source>2460-5859</dc:source>
	<dc:source>2252-6943</dc:source>
	<dc:source>10.15294/ujm.v15i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/ujm/article/view/25549/11868</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Unnes Journal of Mathematics</dc:rights>
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