Perbandingan Kualitas Hasil Klaster Algoritme K-Means dan Isodata pada Data Komposisi Bahan Makanan - Reza Wahyu Wardani; Budi Darma Setiawan; Candra Dewi (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Health is the most important part of human beings. One way that can be done to maintain health to regulate diet. Setting diet can be done by calculating the amount of daily nutrient content that enters body. Problems related to nutrition in community are malnutrition or condition where body doesn't get enough nutrition according to daily needs. This happens because most people dont understand how to regulate and classify food according to portion nutrients body. Fulfillment daily nutrition can be done if food has been in group based on nutritional similarity. Food grouping algorithm is needed so that people can find out enough daily nutritional alternatives based on potential commodities in Indonesia. Data used in study amounted 250 data sourced from Indonesian Ministry of Health regarding composition of food ingredients. Purpose of this study is examine which method is best by comparing K-Means and Isodata clustering algorithms based on the quality of clusters produced. Cluster quality measurements using Silhoutte Coefficient method. Based on tests conducted, Silhoutte Coefficient K-Means algorithm is 0.996762 and Isodata algorithm is 0.996910. Both these methods have small difference value but Isodata algorithm has greater Silhouette Coefficient value than K-Means algorithm in clustering Food Composition Data.