Comparative Analysis of Machine Learning Algorithms for classification about Stunting Genesis

Byna, Agus (2020) Comparative Analysis of Machine Learning Algorithms for classification about Stunting Genesis. In: NS-UNISM 2019, 23 November 2019, Banjarmasin, South Kalimantan, Indonesia.

[thumbnail of PDF]
Preview
Text (PDF)
eai.23-11-2019.2298349.pdf - Published Version

Download (377kB) | Preview

Abstract

Background The use of machine learning is very much needed for health experts as data and information processing to make it easier to analyse automatically so as to produce accuracy in solving problems, application of machine learning with comparative 3 algorithms to solve stunting problems because toddlers in Indonesia are still high, especially at age 2 -3 years. Seen from a number of factors that are at risk of causing stunting. Instrument is needed in a Machine Learning. The goal (1). In addition to providing knowledge in the field of Informatics, it is also useful for health experts in managing data in making decisions so as to facilitate analysis automatically. (2). Can reduce the impact on the incidence of stunting. Methods Comparison of three algorithms in classification the results of three algorithms that were compared yielded an accuracy of 86% AUC 0.85 for the Decision Tree algorithm with a diagnosis level of Good classification, Algorithm KNN with an accuracy of 58.7% AUC 0.57 fail classification, Algorithm Naïve Bayes with 55% AUC accuracy 0.51, using 13 stunting data variables

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: genesis stunting decision tree knn naïve bayes machine learning
Subjects: H Social Sciences > H Social Sciences (General)
Depositing User: EAI Editor IV
Date Deposited: 02 Aug 2021 14:37
Last Modified: 02 Aug 2021 14:37
URI: https://eprints.eudl.eu/id/eprint/5697

Actions (login required)

View Item
View Item