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Comparison and Analysis of Classification Algorithm Performance for Nutritional Status Data

by Herman Yuliansyah, Sri Winiarti, Ika Arfiani, Norma Sari
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 20
Year of Publication: 2020
Authors: Herman Yuliansyah, Sri Winiarti, Ika Arfiani, Norma Sari
10.5120/ijca2020920157

Herman Yuliansyah, Sri Winiarti, Ika Arfiani, Norma Sari . Comparison and Analysis of Classification Algorithm Performance for Nutritional Status Data. International Journal of Computer Applications. 176, 20 ( May 2020), 14-20. DOI=10.5120/ijca2020920157

@article{ 10.5120/ijca2020920157,
author = { Herman Yuliansyah, Sri Winiarti, Ika Arfiani, Norma Sari },
title = { Comparison and Analysis of Classification Algorithm Performance for Nutritional Status Data },
journal = { International Journal of Computer Applications },
issue_date = { May 2020 },
volume = { 176 },
number = { 20 },
month = { May },
year = { 2020 },
issn = { 0975-8887 },
pages = { 14-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number20/31314-2020920157/ },
doi = { 10.5120/ijca2020920157 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:01.800939+05:30
%A Herman Yuliansyah
%A Sri Winiarti
%A Ika Arfiani
%A Norma Sari
%T Comparison and Analysis of Classification Algorithm Performance for Nutritional Status Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 20
%P 14-20
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nutritional status data is essential data in analyzing early childhood growth and development. This study conducts experiments based on classification algorithms to predict the nutritional status of early childhood. The nutritional status data is analyzed for early childhood with three class labels are nutritional status based on weight for age, height for age and weight for height. By knowing the best and suitable algorithm, in this case, the algorithm analysis results can be extended to the basis of software development to predict the nutritional status of early childhood. The study results are comparisons of classification algorithms such as Support Vector Machine, K-Nearest Neighbors, Random Forest, Decision Tree, and Naïve Bayes. This study uses the split test method by separating the dataset into training sets and test sets by determining the parameters of the amount of training data that is 10%, 20%, 30%, 40%, and 50%. The experimental results show that the Decision Tree algorithm is superior for weight data for age and height for an age while K-Nearest Neighbors is superior for weight data for height.

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Index Terms

Computer Science
Information Sciences

Keywords

Nutritional Status Early Childhood Support Vector Machine K-Nearest Neighbors Random Forest Decision Tree Naïve Bayes

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