Pendekatan Klasterisasi K-Means untuk Mengkategorikan Risiko Obesitas dengan Rapidminer - Rahman Santosa, Ravansa; Amali; Sasi Kirana, Anindia; Muhana Aydin Nashif, Hamim (Jurnal SIGMA, 2024)
Millions of people around the world have obesity problems, which increase the risk of various chronic diseases. Clustering method with k-means algorithm is used in this study to analyze obesity patterns based on behavioral and physical data. The obesity risk data was processed using RapidMiner after being obtained from the Kaggle source. The “Nominal to Numerical” operator converts nominal attributes into numerical data, which allows the k-means algorithm to be used. The elbow method was used to select the ideal number of clusters. The clustering results identified three main groups based on healthy lifestyle, high obesity risk, and different levels of physical activity. This analysis improves our understanding of obesity patterns and the factors that contribute to the condition. The results from this study can help in the creation of better prevention and intervention methods to effectively address obesity.