Perbandingan Hybrid Genetic K-Means++ dan Hybrid Genetic K-Medoid untuk Klasterisasi Dataset EEG Eyestate

Muhammad Ezar Al Rivan (STMIK Global Informatika MDP)
Giovani Prakasa Gandi (STMIK Global Informatika MDP)
Fendy Novianto Lukman (STMIK Global Informatika MDP)

Petir
Vol 14 No 1 (2021): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)

K-Means++ and K-Medoids are data clustering methods. The data cluster speed is determined by the iteration value, the lower the iteration value, the faster the data clustering is done. Data clustering performance can be optimized to get more optimal clustering results. One algorithm that can optimize cluster speed is Genetic Algorithm (GA). The dataset used in the study is a dataset of EEG Eyestate. The optimization results before hybrid GA on K-Means++ are the iteration average values is 11.6 to 5,15, and in K-Medoid are the iteration average values decreased from 5.9 to 5.2. Based on the comparison of GA K-Means++ and GA K-Medoids iterations, it can be concluded that GA - K-Means++ better