English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The purpose of this research is classify nutritional status of toddlers with algorithm fuzzy k-nearest in every class (FK-NNC). The FK-NNC algorithm is a modification concept of the nearest K neighbor for each class. The largest of the class on membership value will be selected as prediction classes. The optimal K value in FK-NNC algorithm uses k-fold cross validation. The optimal K values are searched by experimenting 1-fold cross validation, 4-fold cross validation and 10-fold cross validation. Accuracy rate of classify nutritional status of toddlers at the Wonorejo Health Center using the FK-NNC algorithm with an optimal K value. The results of this research obtained the optimal K value used in the FK-NNC algorithm at the Wonorejo Health Center is K=8 with 1-fold cross validation experiment. The value of K=8 was applied to the FK-NNC algorithm with a 1-fold cross validation experiment to predict class of nutritional status of toddlers in the Wonorejo Health Center. The percentage of accuracy produced as much as 100% with data proportion 90:10.

Classification of nutritional status of toddlers using fuzzy k-nearest neighbor in every class (FK-NNC)