Open AccessApplication Of Naive Bayes Algorithm In Classification Of Child Nutrition At The Simalungun Health Office
Author(s) -
Susi Septi Hardiani,
M. Safii,
Dedi Suhendro
Publication year - 2021
Language(s) - English
DOI - 10.35335/ijobas.v10i3.57
Subject(s) - naive bayes classifier , class (philosophy) , bayes' theorem , computer science , point (geometry) , artificial intelligence , machine learning , algorithm , statistics , medicine , mathematics , bayesian probability , support vector machine , geometry
Toddlers are among the most vulnerable groups to nutritional problems when viewed from the point of view of health and nutrition problems, while at this time they are experiencing a cycle of relatively rapid growth and development. .7% is quite high where the number of births is relatively large. Researchers try to classify 10 toddlers using WEKA to find out whether they have nutritional disorders or are normal by using 5 attributes as system input and a class namely nutrition which divides this class into 4 namely bad, less, good and more with the amount of training data 219 data then data compared with the actual nutritional conditions and obtained an accuracy of 60% and an error of 40% with these results it can be concluded that the accuracy is not too good. Based on this, it is hoped that the results of this classification can help further research in classifying the nutrition of children under five.