Open AccessClassification System Of Toddler Nutrition Status using Naïve Bayes Classifier Based on Z- Score Value and Anthropometry Index
Author(s) -
Tiara Eka Putri,
Ridho Taufiq Subagio,
KUSNADI KUSNADI,
Petrus Sobiki
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1641/1/012005
Subject(s) - toddler , anthropometry , naive bayes classifier , malnutrition , bayes classifier , artificial intelligence , statistics , medicine , mathematics , computer science , psychology , developmental psychology , support vector machine
The system of nutritional status assessment for a toddler is crucial to monitor the growth of a toddler. This present study was carried out to build a classification system for determining the assessment of toddler nutritional status using naive bayes classifier based on value the z-score and index of Anthropometry. The data was used to perform classification include gender, age, height and weight. The data was calculated using the z-score to get nutritional status based on anthropometric indices-weight-for-age, height-for-age, weight-for-height for classified use Naive Bayes Classifier . This study used 225 data of toddlers. Testing system used 55 data as training and 175 data as testing with 100% accuracy. The results of this study was a system that could be used to perform classification of nutritional status based on a combination of three anthropometric indices using the Naive Bayes Classifier . Naive Bayes Classifier performed classification interpretation of nutritional toddler consisting of malnutrition, normal and over-nutrition. This study showed that the classification of nutritional status was on 175 data generating the highest percentage was malnutrition of 44.58%.