Analisis Cluster Pada Produk Mie Instan Berdasarkan Komposisi Yang Terkandung Dengan Menggunakan Metode Ward

  • Faza Syahrudin Sam Laboratorium Statistika Komputasi FMIPA Universitas Mulawarman
  • Syaripuddin Syaripuddin Laboratorium Matematika Komputasi, FMIPA Universitas Mulawarman
  • Wasono Wasono Laboratorium Matematika Komputasi, FMIPA Universitas Mulawarman

Abstract

Cluster analysis is a grouping of data (objects) based on only the information found in the data that describes the object and the relationships between data. The variance method commonly used is the Ward method where the average for each cluster is calculated. At each stage, the two clusters that have the smallest increase in sum of squares in the cluster are combined.. Some compositions of ingredients in noodles, for example, fat, protein, carbohydrates, food fiber, sugar and sodium. The composition of the noodles that are dangerous one of which is Monosodium Glutamate (MSG). The purpose of this research is to find out how many clusters are formed based on the composition of the content of instant noodle products. Based on the results of cluster research formed based on the composition of the contents of 43 instant noodle samples are 9 clusters where the first cluster consists of 2 members, the second cluster consists of 7 members, the third cluster consists of 5 members, the fourth cluster consists of 7 members, the fifth cluster consists of 6 members, the sixth cluster consists of 4 members, the seventh cluster consists of 4 members, the cluster the eighth consists of 1 member and the ninth cluster consists of 7 members.

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Published
2021-06-21
How to Cite
SAM, Faza Syahrudin; SYARIPUDDIN, Syaripuddin; WASONO, Wasono. Analisis Cluster Pada Produk Mie Instan Berdasarkan Komposisi Yang Terkandung Dengan Menggunakan Metode Ward. EKSPONENSIAL, [S.l.], v. 12, n. 1, p. 53-58, june 2021. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/759>. Date accessed: 20 apr. 2024. doi: https://doi.org/10.30872/eksponensial.v12i1.759.
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Articles