Metode Machine Learning untuk Klasifikasi Data Gizi Balita dengan Algoritma Naïve Bayes, KNN dan Decision Tree

Ramadhani Ramadhani; Ramadhanu Ramadhanu

doi:10.24176/simet.v15i1.10679

Metode Machine Learning untuk Klasifikasi Data Gizi Balita dengan Algoritma Naïve Bayes, KNN dan Decision Tree

Ramadhani Ramadhani
Ramadhanu Ramadhanu

Abstract

Stunting in toddlers is a serious health problem, Stunting is a term used to describe the delay in physical development of children from conception or formation to the age of 2 years, resulting in height lower than their chronological age. Stunting in toddlers can be caused by socioeconomic conditions, maternal nutrition during pregnancy, infant diseases, and inadequate infant nutritional intake. Infectious diseases are the most direct and common cause of growth failure in young children, and effective strategies are needed to reduce risk factors for developmental delays in children under the age of five. The method to overcome this problem is a machine learning (ML) classification method that uses Naive Bayes, KNN and Decision Tree algorithms to classify nutritional data of young children, thus helping to overcome developmental delays, early intervention. The result of this study is the highest precision poor naïve bayes algorithm performance found in the malnutrition category at 38% and recall there are two categories that cannot be identified. The KNN algorithm has one category of nutritional risk that cannot be identified precision and recall, KNN is higher than naïve bayes at 40%. The Decision Tree looks normal and has 48% accuracy, with better recall and precision than Naive Bayes and KNN

Keywords

klasifikasi;Naive bayes;gizi balita;stunting;knn;machine learning

Teks Lengkap:

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Referensi

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DOI: https://doi.org/10.24176/simet.v15i1.10679