Integrasi K-Modes dalam Analisis Data Gizi Balita untuk Model Klasifikasi Risiko Stunting - Desyanti, Desyanti; Renaldi, Reno; Mesran, Mesran (Building of Informatics, Technology and Science (BITS), 2025)
The nutritional status of toddlers is an important indicator in assessing child growth and development and is closely related to the risk of stunting. However, the process of recording and classifying nutritional status at the Bukit Kapur Community Health Center is still done manually, making it prone to analysis delays and data processing errors. This study aims to implement the K-Modes algorithm in classifying toddler nutritional status based on categorical data, such as age, weight, and height. Toddler data were collected from the Bukit Kapur Community Health Center and underwent pre-processing, data transformation, and the application of the K-Modes algorithm to determine toddler nutritional groups. The results showed that the K-Modes algorithm was able to group toddler data into three main categories: well-nourished, at-risk of overnutrition, and overnourished. The majority of toddlers fell into the well-nourished category (98 toddlers), while only a small proportion fell into the at-risk of overnutrition and overnourished categories (1 toddler each). These findings indicate that the K-Modes method is effective in classifying toddler nutritional status based on categorical data and can assist health workers in monitoring child growth and preventing stunting.