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The application of medoid-based cluster validation in desirable dietary pattern data

Budiaji, Weksi and Riyanto, Rifqi Ahmad and Suherna, Suherna (2021) The application of medoid-based cluster validation in desirable dietary pattern data. Journal of Physics: Conference Series ICMSDS 2020.

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A desirable dietary pattern (DDP) index is an index to measure the balance and variance of the nutrition intake of an individual. This index is composed of the calory values of protein, fat, and carbohydrates. Grouping individuals based on the DDP index is required to measure and improve an individual food security state. We took 14 individual purposively as samples to fill a set of DDP questioner. They were asked about their daily food consumption. They were grouped based on the DDP variables. A 3-dimensional plot showed that there were three to four clusters. Then, medoid-based partitioning algorithms, namely partitioning around medoids (PAM) and simple k-medoids (SKM), were applied in the data set. The inputted distances were generalized distance function to vary the distance options. The cluster results were then validated by medoid-based shadow value validation. This index was comparable to the 3-dimensional plot such that four clusters were opted as the most suitable number of clusters. The barplot of the cluster results showed that cluster 1 was characterized by an abundance of fat, while cluster 2 had very sufficient carbohydrates. Cluster 3 and 4 were two clusters with opposite characteristics where the former had a shortage of protein, fat, and carbohydrates, while the latter had an abundance of them.

Item Type: Article
AuthorBudiaji, Weksi198303082006041004
AuthorRiyanto, Rifqi A199108052019031010
AuthorSuherna, Suherna196908192002121001
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: 04-Fakultas Pertanian > 54201-Program Studi Agribisnis
Depositing User: Dr. Weksi Budiaji
Date Deposited: 21 Mar 2022 21:33
Last Modified: 23 Mar 2022 09:00

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