Implementasi K-Means Untuk Pengelompokan Makanan Cepat Saji Bagi Penderita Penyakit Obesitas - Pradvenanta, Yoannes Dion; Prathivi, Rastri (Forum Kerjasama Pendidikan Tinggi, 2024)
One in eight people in the world lives with obesity, a statistic that is worrying as it shows a significant increase compared to 1990. Obesity in adults has more than doubled, and obesity among adolescents and children has quadrupled. One factor in obesity is poor food quality. A lot of people who don't pay much attention to the quality of their food one of them is eating fast food because fast food consumption can be said to be good if the meal frequency is 1 time a week, if more than that and excess is said not good. Thus, there is a need for a fast food grouping model that helps obese people choose fast foods. The K-means algorithm is one of the ideal models for grouping fast foods. The results of the analysis using the elbow method show k=5, then consider three evaluations against the k=5 value: Sum Square Error (SSE), Silhouette Score, and Davies Bouldin Index (DBI). The results were data segmented taking into account the negative and positive nutrient content for obese patients. The data segmentation results found a fairly healthy cluster on label_0 with 244 data and an unhealthy cluster in label_2 with 25 data. From the cluster label_0, 244 of the data could be a healthy fast food choice for obesity patients