KLASIFIKASI STATUS GIZI PADA PERTUMBUHAN BALITA MENGGUNAKAN K-NEAREST NEIGHBOR (KNN)

Abstract

The nutritional status for babies is something that is very important for parents to know, because
there are still many cases of under-five malnutrition in Indonesia that never go away. Especially in
the Kijang area at the Sei Lekop Health Center, there are still many toddlers who have unbalanced
nutrition, due to lack of parental knowledge about toddler nutrition. In this study, the K-Nearest
Neighbor method was used to classify the nutritional status of toddlers and can also create a
Nutritional Status Classification System for Toddler Growth using the KNN (K-Nearest Neighbor)
Method. Toddler nutrition data uses 4 classifications, namely: More, Good, Less, Bad. The amount
of data used for this study is 170 data, of which 80% are training data totaling 136 data, and 20%
are test data totaling 34 data. The level of accuracy of the results of this test is to produce an accuracy
rate of 73.53% using the K-Nearest Neighbor algorithm