Prediction of Stunted Toddlers Using K-Nearest Neighbor Algorithm in Kamarang Lebak Village - Amida, Anggi Fitria; Permana, Sandy Eka; Pratama, Denni; Anam, Khaerul; Rinaldi, Ade Rizki (Cattleya Darmaya Fortuna, 2023)
Stunting refers to a condition where toddlers (under five years old) experience growth failure, resulting in height and weight below the average for their age. The focus of this research is on the situation in Kamarang Lebak Village, where the number of stunted toddlers is notably significant. However, there has yet to be a study accurately predicting the factors differentiating stunted toddlers from those growing normally, thus lacking clarity on how accurate such predictions are in identifying toddlers vulnerable to stunting. The data collection method employed in this study involves observational techniques, with researchers visiting the Kamarang health center in Greged Sub-District, Cirebon Regency, to gather necessary information and data. This research implements the K-Nearest Neighbor Algorithm method to predict stunted toddlers and is supported by the Knowledge Discovery in Database approach, involving steps such as data selection, collection, transformation, data mining processes, and evaluation. It is anticipated that this research will serve as a foundation for public health practitioners, especially community health workers and village midwives in the area, to plan more focused and efficient intervention programs addressing toddler stunting issues. The results of this study indicate that the K-nearest neighbor algorithm demonstrates good performance with an accuracy of 97.16%. Stunting precision reaches 95.60%, normal precision reaches 98.82%, stunting recall reaches 98.86%, and normal recall reaches 95.45%.