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All Journal Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population)
Lilik Inayati
Mayangrejo Village Midwife, Kalitidu Health Center, 62152 Bojonegoro, East Java, Indonesia

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IMPLEMENTATION OF CLUSTERING USING K-MEANS METHOD TO DETERMINE NUTRITIONAL STATUS Stefanny Surya Nagari; Lilik Inayati
Jurnal Biometrika dan Kependudukan (Journal of Biometrics and Population) Vol. 9 No. 1 (2020): JURNAL BIOMETRIKA DAN KEPENDUDUKAN
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jbk.v9i1.2020.62-68

Abstract

Cluster analysis aims to classify data objects into two categories: objects that are similar in characteristics in one cluster and objects that are different in characteristics with the other objects of another cluster. K-Means is a method included in the distance-based clustering algorithm that starts by determining the number of desired clusters. Malnutrition is one of the biggest concerns in Indonesia. According to Riskesdas 2018 data, as many as 17.7% infants under 60-month-old are still having problems with nutrition intake while 3.9% are having malnutrition. This might result in higher death rate. This research was conducted to classify the nutritional status of infants under 60-month-old conducted by the C-Means Clustering method. This research is non-reactive, using secondary data in Ponkesdes Mayangrejo, Bojonegoro without direct interaction with the subject. This study concluded that the grouping of nutritional status is possible by using K-Means with 4 clusters formed which are 23 malnourished toddlers, 17 undernourished toddlers, 7 nourished toddlers, and 10 over-nourished toddlers.
Co-Authors Stefanny Surya Nagari