Child Nutrition Prediction for Stunting Prevention Using K-Nearest Neighbor (K-NN) Algorithm

Chandra Kirana; Delpiah Wahyuningsih; Louis Michael

doi:10.63158/journalisi.v7i4.1322

Chandra Kirana Institut Sains dan Bisnis Atma Luhur, Indonesia
Delpiah Wahyuningsih Institut Sains dan Bisnis Atma Luhur, Indonesia
Louis Michael Institut Sains dan Bisnis Atma Luhur, Indonesia

DOI: 10.63158/journalisi.v7i4.1322

Keywords: K-Nearest Neighbor, child nutrition prediction, stunting prevention, classification, artificial intelligence

Abstract

Stunting is a significant public health issue in Indonesia, affecting both children's physical growth and cognitive development. This study aims to develop a child nutritional status prediction application using the K-Nearest Neighbor (K-NN) algorithm as an early detection tool for stunting prevention. The model classifies nutritional status into five categories: good nutrition, poor nutrition, undernutrition, overnutrition, and obesity, using anthropometric data such as age, weight, height, and gender. The dataset comprises 49,766 samples of children aged 0–5 years from the Bangka Belitung Islands Provincial Health Office. The data processing included normalization, feature selection, and k-value testing to optimize model performance. Evaluation results showed that K-NN with k = 2 achieved 92% accuracy, with the best precision and recall in the good nutrition category (0.94 and 0.99). However, performance in minority categories like malnutrition remains low due to data imbalance. The weighted averages for precision, recall, and F1-score were 0.90, 0.92, and 0.90, respectively. This research's novelty lies in integrating the K-NN model into a mobile application, enabling real-time nutritional status assessment for health workers, improving fieldwork efficiency, and facilitating early detection and monitoring.

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