CLASSIFICATION OF TODDLER NUTRITIONAL STATUS USING A BINARY CLASSIFICATION TREE WITH ALGORITHMSQUICK, UNBIASED, EFFICENT, STATISTICAL TREE

Abstract

Determination of nutritional status is very important in helping to monitor the state of nutritional health growth in toddlers every time. In this study, there were 70 identity data for toddlers for the 2022 period obtained from the KB Counseling Center in the Pegajahan sub-district, in Sukasari Village. There are four independent variables used, namely gender, health insurance, weight, and height. The purpose of this study is to determine the classification that is formed and the accuracy of the resulting classification on the nutritional status of toddlers. Classification which is part of data mining can make decisions on the nutritional status of toddlers faster and more efficiently. QUEST method (Quick, Unbiased, Efficient, Statistical Trees) is one of the statistical methods that can be used to form a decision tree and classify an object using a separator algorithm that produces a binary tree. From the results of the classification there is a variable height (𝑥!) as the limiting variable. in the early stages of insulation, the parent node which consists of 70 toddler data. Variables are partitioned based on height into two nodes, namely node (1) and node (2). Node (1) is a node containing 25 children under five with a height of more than 85.79 cm, while node (2) is a node for 45 children under five with a height less than or equal to 85.79 cm. in the next process, the blocking is terminated. the overall value of the accuracy of the classification of trees formed is 95.7%. Thus, the probability of misclassification of the tree is 4.3%, which means that this classification tree is optimal.