IMPLEMENTATION OF NAIVE BAYES CLASSIFIER AND SUPPORT VECTOR MACHINE FOR STUNTING CLASSIFICATION IMPLEMENTATION OF NAIVE BAYES CLASSIFIER AND SUPPORT VECTOR MACHINE FOR STUNTING CLASSIFICATION

NILAM WAHDIAZ AZANI, - IMPLEMENTATION OF NAIVE BAYES CLASSIFIER AND SUPPORT VECTOR MACHINE FOR STUNTING CLASSIFICATION IMPLEMENTATION OF NAIVE BAYES CLASSIFIER AND SUPPORT VECTOR MACHINE FOR STUNTING CLASSIFICATION. Indonesian Journal of Computer Science, 13. ISSN 2549-7286 (Submitted)

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Abstract

Stunting is a condition when a child's physical growth and development are stunted or delayed due to a lack of adequate nutritional intake over a long period of time, especially during the early years of life. Indonesia still has a stunting prevalence rate above the WHO standard, which is at 21.6%. 2020 UN statistics recorded more than 149 million (22%) toddlers worldwide were stunted, of which 6.3 million were early childhood or stunted toddlers were Indonesian toddlers. This study aims to create a classification model using Data Mining Algorithms NBC and SVM to analyze and describe the class of a total of 2018 toddler nutritional status data in Lima Puluh Kota Regency. The results of this study are expected to be an evaluation of whether the stunting prevention program implemented has been successful, and can be the basis for creating the next program.

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