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Desty, Hygiana Prima
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Computer Science & IT

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Diagnosa Stunting Pada Balita Menggunakan Metode Naive Bayes Untuk Sistem Pakar Desty, Hygiana Prima; Marpaung, Noveri Lysbetti
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 2 (2024): June 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.16.2.2024.107-123

Abstract

Stunting is a chronic nutritional problem that impacts intelligence, productivity, and susceptibility to diseases in toddlers. According to the 2022 Indonesia Nutritional Status Survey (SSGI), the prevalence of stunting in Indonesia reached 21.6%. In line with the Indonesian government's efforts to reduce the prevalence of stunting to 14% by 2024, as per Presidential Regulation No. 72 of 2021, early detection and proper treatment of stunted children are essential. This study implements the Naïve Bayes method to predict the nutritional status of toddlers using parameters such as age, weight, height, head circumference, and upper arm circumference. The expert system is designed to integrate expert knowledge into a computer, assisting healthcare professionals in quickly and accurately diagnosing stunting and enhancing parental education on stunting, particularly at the study site in Puskesmas Pembantu Alam Raya, Pekanbaru City. Data collected directly from the study site comprised 340 records, with 238 training data and 102 testing data. The test results using a confusion matrix table showed a precision value of 50%, recall 50%, error rate 3.9%, and accuracy of 97.05%. The system is built using PHP, the CodeIgniter framework, and MySQL as the database. The implementation of the expert system using the Naïve Bayes method in this study is expected to aid in making accurate policies for the prevention and management of stunting in toddlers.
Co-Authors Noveri Lysbetti M