Perbandingan Metode K-Means dan GA K-Means untuk Clustering Dataset Heart Disease Patients - Muhammad Ezar Al Rivan; Randy Andreo Sonaru (Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP, 2022)
Heart disease is a condition which heart as vital human organ is disordered and doesn’t function properly. Heart disease is one of deadliest diseases in the world and the leading cause of death globally, taking an estimated 17.9 million lives each year. In this study, heart disease patients’ data were clustered to see the characteristic and similarities of each patient. The dataset used in the study is Heart Disease Patients dataset, which consists of 303 patients’ medical data with 11 features. Clustering method used in the paper are K-Means and GA K-Means. Genetic Algorithm is used to optimized the initial centroid for K-Means clustering. The results were evaluated by noting the iteration, inter cluster, and intra cluster of each clustering method. Genetic algorithm is able to optimize the K-Means method which can be seen in iteration average, from 13,4 to 12,5 iteration with the decreasing of the maximum iteration from 21 to 17 iterations. Based on the calculation of inter cluster and intra cluster, the intra cluster results of GA K-Means tend to be better than K-Means and for inter cluster, there is a very little different result, where K-Means method inter cluster average slightly better than GA K-Means.