BOOTSTRAP AGGREGATING (BAGGING) REGRESI LOGISTIK ORDINAL UNTUK MENGKLASIFIKASIKAN STATUS GIZI BALITA DI KABUPATEN KLUNGKUNG
ABSTRACT: This research was
conducted to determine the variables that significantly influence nutritional
status of children based on indicators that defined as height for age (H/A) and
to classify children nutritional status into normal, short or very short
categories. Height for age (H/A) is indicator used to describe the
circumstances of malnutrition short. Short children (stunting) is children who
fail to reach optimal growth. The secondary data was list of 116 data of
children aged 24-59 months at UPT. Puskesmas Klungkung I in 2015. The method
was used was ordinal logistic regression and bagging ordinal logistic regression.
Based on the research results, it was obtained variables children body length
at birth, birth weight, and length of mid-upper arm circumference (MUAC) in
pregnant woman were significantly affects the nutritional status of children by
the classification accuracy level of ordinal logistic regression and
misclassification . Classification accuracy of ordinal logistic regression can
be improved by bagging ordinal logistic regression method. Bagging works well
on classification method which has unstable procedures. One of classification
method which has unstable procedures is ordinal logistic regression. Bagging
ordinal logistic regression method by 501 times replication capable to improve
classification accuracy of ordinal logistic regression model from to ,
increased .
Penulis: Palupi Purnama Sari
Kode Jurnal: jpmatematikadd160176
