Comparison of NBC and KNN in Classifying Stunting in Children in Rural Areas - Betrisandi Betrisandi; Rahmat Thaib (Jambura Journal of Electrical and Electronics Engineering, 2026)
Stunting is one of the chronic nutritional problems that remains a serious concern in Indonesia. Children who experience stunting not only experience physical growth retardation, but also cognitive development disorders that have the potential to reduce intelligence, academic achievement, and productivity in adulthood. The problem in this study is the high prevalence of stunting in children in rural areas. The purpose of this study is to analyse the performance of the Naïve Bayes Classifier (NBC) and K-Nearest Neighbour (KNN) and compare the performance of the two methods to determine the most optimal method for classifying stunting status in children in accordance with the Research Master Plan with a focus on engineering and technology for improving ICT content and the research topic of big data technology development. The research methods used included data collection through observation and interviews. Data processing and analysis were carried out by comparing the NBC and KNN methods in classifying child stunting. The results of this study indicate that the NBC method has higher accuracy, namely 95.24% and an F1-score of 97%, compared to the KNN method, which has an accuracy of 76.19% and an F1-score of 86%. Therefore, the KNN method is more optimal for use in classifying stunting in children.