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

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 optimiz...

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Bibliographic Details
Published inPETIR Vol. 14; no. 1; pp. 103 - 113
Main Authors Al Rivan, Muhammad Ezar, Gandi, Giovani Prakasa, Lukman, Fendy Novianto
Format Journal Article
LanguageEnglish
Published 02.10.2020
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Summary: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
ISSN:1978-9262
2655-5018
DOI:10.33322/petir.v14i1.953