Penerapan K-Nearest Neighbor Untuk Klasifikasi Status Gizi Balita Di Puskesmas Karang Rejo - Nurfadilah, Asriani; Anto, Anto; Fadlan, Muhammad (Journal of Big Data Analytic and Artificial Intelligence, 2025)
The issue of nutritional status in toddlers is one of the crucial concerns in the health sector, particularly in efforts to prevent stunting and malnutrition. At Karang Rejo Public Health Center, the assessment of toddlers’ nutritional status is still conducted manually by relying on weight-based estimation, which makes the classification process less efficient and increases the risk of inaccuracies in determining nutritional status. This study aims to apply the K-Nearest Neighbor (K-NN) algorithm as a faster and more accurate method for classifying toddlers’ nutritional status. The research stages include data collection, data processing, and the application of the K-NN algorithm. The data used consist of 100 toddler records, including the variables of weight-for-age (BB/U), height-for-age (TB/U), and weight-for-height (BB/TB) as the basis for determining nutritional status. The results show that the application of the K-NN algorithm with an optimal k value of 3 is able to produce nutritional status classifications that are consistent with doctors’ assessments. Performance evaluation using a Confusion Matrix on the test data yields an accuracy of 100%, with precision and recall values also reaching 100% for each nutritional status category. These findings indicate that all test data are classified correctly. Therefore, the application of this method is expected to assist healthcare workers at Karang Rejo Public Health Center in diagnosing and monitoring toddlers’ nutritional status more effectively and efficiently.