Proceedings of the First National Seminar Universitas Sari Mulia, NS-UNISM 2019, 23rd November 2019, Banjarmasin, South Kalimantan, Indonesia

Research Article

Comparative Analysis of Machine Learning Algorithms for classification about Stunting Genesis

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  • @INPROCEEDINGS{10.4108/eai.23-11-2019.2298349,
        author={Agus  Byna},
        title={Comparative Analysis of Machine Learning Algorithms for classification about Stunting Genesis },
        proceedings={Proceedings of the First National Seminar Universitas Sari Mulia, NS-UNISM 2019,  23rd November 2019, Banjarmasin, South Kalimantan, Indonesia},
        publisher={EAI},
        proceedings_a={NS-UNISM},
        year={2020},
        month={7},
        keywords={genesis stunting decision tree knn na\~{n}ve bayes machine learning},
        doi={10.4108/eai.23-11-2019.2298349}
    }
    
  • Agus Byna
    Year: 2020
    Comparative Analysis of Machine Learning Algorithms for classification about Stunting Genesis
    NS-UNISM
    EAI
    DOI: 10.4108/eai.23-11-2019.2298349
Agus Byna1,*
  • 1: Universitas Sari Mulia, Indonesia
*Contact email: agusbyna@unism.ac.id

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