PENGARUH ALGORITMA ARTIFICIAL BEE COLONY DALAM MENENTUKAN CENTROID AWAL ALGORITMA K-MEANS

NOPREN, NOPREN and Jambak, M. Ihsan (2020) PENGARUH ALGORITMA ARTIFICIAL BEE COLONY DALAM MENENTUKAN CENTROID AWAL ALGORITMA K-MEANS. Undergraduate thesis, Sriwijaya University.

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Abstract

K-means clustering algorithm is an algorithm with the concepts of similarity and dissimilarity. In general, initial centroid determination of the k-means clustering algorithm is carried out randomly so that it allows optimum conditions to occur only to the nearest candidate solution (local optimum) not to all existing solutions (global optimum). Therefore, to improve the quality of clustering results, another algorithm is needed to help calculate the initial centroid value of the k-means algorithm. One optimization algorithm is the artificial bee colony algorithm, whose working process simulates the intelligent foraging behavior of bees. This study will compare the application of the Artificial Bee Colony algorithm to the initial centroid determination of the K-Means Clustering algorithm. The results obtained from the comparison of the quality of the results of grouping the k-means algorithm with the initial centroid optimized by the artificial bee colony algorithm experienced a percentage decrease in DBI value of 49.58% for data without reduction, and decreased by 58.15% for the data already reduced. On the other hand, the k-means algorithm with initial centroid optimized by the artificial bee colony algorithm is able to speed up the convergence process when compared to the k-means algorithm with random initial centroids

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Similarity, Dissimilarity, Centroid, Local Optimum, Global Optimum, K-Means Clustering, Artificial Bee Colony
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 7818 not found.
Date Deposited:	07 Sep 2020 06:14
Last Modified:	07 Sep 2020 06:14
URI:	http://repository.unsri.ac.id/id/eprint/34554

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