Mulyati, Addina (2023) Penerapan Data Mining Untuk Memprediksi Stunting Pada Balita Menggunakan Naive Bayes. Other thesis, Universitas Islam Riau.

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Abstract

tunting is a disorder of growth and development in children caused by long-term chronic malnutrition. Children with stunting conditions tend to experience stunted physical growth and do not reach height in accordance with the growth standards of children their age. Stunting conditions can affect brain development, cognitive abilities, immune system, to the risk of chronic disease attacks. This study aims to apply data mining techniques, especially the Naïve Bayes method in predicting stunting in toddlers aged 0-59 months. The Naïve Bayes method is used to build predictive models that can identify children at risk of stunting based on the variables used. This research is implemented in the form of a website-based programming system using Python programming language and streamlit framework. From testing using 174 data, an accuracy value of 95.40%, a precision value of 95.36%, a recall value of 96.40 % and an F1 score of 95.87%. The results of the tests conducted show that the Naïve Bayes method can be used in predicting stunting in toddlers.

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