Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNadika Sigit Sinatryaid
dc.contributor.authorLuh Kesuma Wardhaniid
dc.description.abstractThis research’s goal is to find out the better performance algorithm between K-Means and K-Medoids algorithm. The performance of both algorithm are compared by testing data using Java-based application, Hadoop, and Hive. comparison was conducted in terms of accuracy, execution time and time complexity of the algorithm. In terms of accuracy, K-Medoids is better than K-Means with an average accuracy of 63.24%, while K-Means is 52.11%. In terms of execution time, K-Medoids also has better performance with average speed of 3.1 ms, while K-Means is 3.45 ms. In terms of time complexity algorithms, both algorithms have the result of O (n2). K-Medoids has better performance than K-Means, which K-Medoids has an average value of 310.157, while K-Means has greater value than K-Medoids of 377,886. So the K-Medoids algorithm is superior to K-Means in terms of accuracy, execution time and time
dc.titleAnalysis of K-Means and K-Medoids’s Performance Using Big Data Technologyid
Appears in Collections:Pre Print

Files in This Item:
File SizeFormat 
10.1109@CITSM.2018.8674251.pdf1.01 MBAdobe PDFView/Open

Items in UINJKT-IR are protected by copyright, with all rights reserved, unless otherwise indicated.