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All Journal JOMLAI: Journal of Machine Learning and Artificial Intelligence
Nababan , Refly Natalius
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Computer Science & IT Engineering

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Overview of Infant Nutrition Status Classification with Rough Set Method Napitupulu, Jessica Evonella; Trianda, Dimas; Nababan , Refly Natalius
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 3 (2023): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i3.2893

Abstract

Infant growth and development is an important issue that can be known through nutritional status assessment. A measure of the fulfillment of nutrition in children that can be predicted based on their weight. In assessing the nutritional status of infants, there are concerns in the community about nutritional problems that are good to know, many babies are malnourished and also want to know which children whose nutrition is really ideal]. Rough Set Algorithm can be used as a mathematical tool to overcome uncertainty and imprecise information. This study aims to classify the percentage of nutritional status of infants, using Microsoft Excel and Rosetta version 2.0.0.0 for research and data analysis. The research produced 20 rules in the form of rule patterns as a reference for classifying the nutritional status of infants as poor, less, normal and more. Based on the rules generated, it is concluded that the most influential condition attributes in classifying the nutritional status of infants are gender, age, weight, height and gender, weight, height.
Co-Authors Napitupulu, Jessica Evonella Trianda, Dimas