Open AccessClassification of Toddler Nutrition Using C4.5 Decision Tree Method
Author(s) -
Kartono Pinaryanto,
Robertus Adi Nugroho,
Yanuarius Basilius
Publication year - 2021
Publication title -
international journal of applied sciences and smart technologies
Language(s) - English
Resource type - Journals
eISSN - 2685-9432
pISSN - 2655-8564
DOI - 10.24071/ijasst.v3i1.3366
Subject(s) - toddler , medicine , decision tree , demography , body height , age groups , body weight , pediatrics , psychology , developmental psychology , computer science , artificial intelligence , sociology
Nutrition is very much needed in the growth of toddlers. It is very important to give babies a balanced nutritional intake at the right stage so that the baby grows healthy and is accustomed to a healthy lifestyle in the future. Children under five years of age are a group that is vulnerable to health and nutrition problems. In determining the nutritional status, it can be done in a system manner using the C4.5 decision tree classification method and entering several variables or attributes. The dataset tested was 853 toddlers. Classification is carried out to determine the nutritional status based on the weight/age (BB/U), height/age (TB/U) and weight/height (BB/TB) categories. The attributes used for the classification of BB/U are gender, weight and age. The attributes used for TB/U are gender, body length or height, and age. The attributes used for BB/TB are gender, weight, body length or height, and age. The average accuracy of the BB/U category is 90.16%, the average accuracy of the TB/U category is 76.64%, and the average accuracy of the BB/TB category is 83.83%.