Development of a Naive Bayes-Based Android Application for Early Detection of Wasting-to-Stunting Transition Risk
DOI:
https://doi.org/10.47709/brilliance.v6i4.9495Keywords:
Stunting, Wasting, Android Application, Naive BayesAbstract
Undetected and untreated wasting (acute undernutrition) can progress to stunting (chronic undernutrition), negatively affecting children’s physical growth, cognitive development, and quality of life. This condition remains a public health challenge requiring intervention. In Indonesia, particularly in Pematangsiantar City, North Sumatra, access to information and technology for early detection of nutritional problems in children remains limited. Inadequate management of wasting cases is one factor contributing to the high prevalence of stunting. This study aims to develop an Android application using the Naive Bayes algorithm to detect the risk of wasting progressing to stunting among children under five. The application calculates risk probabilities based on anthropometric data and presents results in real time to support faster, accurate decision-making by healthcare workers. Development included collecting and preprocessing anthropometric data from Singosari Community Health Center (Puskesmas) in Pematangsiantar, identifying predictor variables based on WHO growth standards, developing a prediction model using Naive Bayes, and integrating the model into an Android application. The results indicate that the application can classify the nutritional status of children under five into normal, wasting, and stunting categories with high accuracy. The application provides a simple and user-friendly interface suitable for healthcare workers and the general public. The study concludes that Naive Bayes can effectively support early detection of stunting risk and encourage community participation in prevention efforts. Furthermore, the application has potential to improve healthcare service efficiency, strengthen nutritional monitoring, and contribute to reducing stunting prevalence through timely intervention.
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