Infant health can be known one of them
through the assessment of nutritional
status. In general, Body Mass Index (BMI)
has been used as a method for measuring
the nutritional status of children. If there
are two children who have same body
weight and height, they may have different
nutritional status. Whenever this occurs,
the use of BMI for measuring the
nutritional status shall be deemed less
accurate. The anthropometry will be vital
in measuring the nutritional statuss. The
guidelines for determining the nutritional status Anthropometry
parameters are selected and recommended which includes an
assessment of the age, weight, body length or height. This
research aims to build a model of C4.5 adaboost so it can
recognize patterns and be able to classify the nutritional status
of children into five classes: normal, fat, very fat, thin and very
thin. The variables used in this classification is Gender, Age
(Months), Weight (kg) Height (cm). C4.5 (decision tree) Method
has a good performance in dealing with the classification of
nutritional status but the C4.5 has a weakness in the class
imbalance. Adaboost isone ofboosting methods that could
reduce imbalances class by giving weight to the level of
classification error which may alter the distribution of data.
Experiments carried out by applying the adaboost method C4.5
to obtain optimal results and a good degree of accuracy. The
experimental results obtained from C4.5 method show that
accuracy is 89.53%, the error rate is 10.47%, while the results of
C4.5 with adaboost show 90.23% accuracy and 9.77% error rate.
It can be concluded in the classification of nutritional status of
children with C4.5 and adaboost proven method to solve
problems of class imbalance and improve the high accuracy and
can reduce the level of classification error.
Keywords: Child Nutrition Status Classification,
Antropometrics Index, C4.5, Adaboost.