CLUSTERING MENGGUNAKAN METODE K-MEAN UNTUK MENENTUKAN STATUS GIZI BALITA
Abstract: Malnutrition is one
of the health problems that quite often among toddlers in Indonesia. Trace data
from the WHO, the number of infants who had died from malnutrition in Indonesia
in 2012 was 29 out of 1000 births. Caring parents and village officials (in this
case the officer Public Health Service Center - HEALTH) to monitor the
nutritional indispensable. Research conducted to try to perform a grouping of
50 children in the village of Karang Songo into 5 clusters nutritional status.
Grouping nutritional status of children in the village of Songo Flower using
the K-Means method is done through several stages, namely: the determination of
business objectives, data collection 50 children in the village of Songo coral,
grouping balitake nutritional status in five clusters, namely cluster 1 -
malnutrition; cluster 2 - malnutrition; cluster 3 - good nutrition; cluster 4 -
nutrition; Cluster 5 - obesity, cluster calculations using SPSS software,
analysis of the output data, grouping nutritional status of children using
tables Growth Chart, and the latter tested by comparing the results of the
grouping of K-means algorithm and tables Growth Chart. By comparing the results
of grouping using a table Growth Chart and K-Means algorithm obtained 17 data
have the same group. From this figure it can be concluded that the K-Means
algorithm only has an accuracy score of 34% correct.
Penulis: Windha Mega Pradnya
Dhuhita
Kode Jurnal: jptinformatikadd150853
