OPTIMASI ALGORITMA K-MEANS PADA PENGELOMPOKAN DATA MENGGUNAKAN ALGORITMA ARTIFCIAL BEEOPTIMASI ALGORITMA K-MEANS PADA PENGELOMPOKAN DATA MENGGUNAKAN ALGORITMA ARTIFCIAL BEE COLONY COLONY

AGRITAMA, M. EDU and Rini, Dian Palupi and Saputra, Danny Matthew (2021) OPTIMASI ALGORITMA K-MEANS PADA PENGELOMPOKAN DATA MENGGUNAKAN ALGORITMA ARTIFCIAL BEEOPTIMASI ALGORITMA K-MEANS PADA PENGELOMPOKAN DATA MENGGUNAKAN ALGORITMA ARTIFCIAL BEE COLONY COLONY. Undergraduate thesis, Sriwijaya University.

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Abstract

Grouping or clustering is the process of classifying objects. This information is obtained from data that describes the relationship between objects by using the principle of maximizing similarities between members of one class and creating similarities between classes. One of the grouping methods is the K-Means algorithm. In general, the K-Means algorithm determines the centroid value randomly and gets a solution with the closest value (local optima). To improve the quality of data grouping and the value of the K-Means centroid, optimization algorithms are needed, one of which is the Artificial Bee Colony algorithm that follows the simulation of the intelligent behavior of a swarm of bees. This study will compare the K-Means algorithm only and the K-Means algorithm optimized with the Artificial Bee Colony algorithm. The results compared are the DBI (Davies-Bouldin Index) values. The K-Means algorithm only has the smallest average DBI of2,2696, while the K-Means algorithm is optimized by the Artificial Bee Colony algorithm with the smallest average DBI of 0,0504 . It was found that the K-Means algorithm which was optimized with the K-Means algorithm got a better grouping value than the K-Means algorithm only.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Clustering, K-Means, Artificial Bee Colony, Davies Bouldin Index.
Subjects:	Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	M. Edu Agritama
Date Deposited:	09 Aug 2021 08:18
Last Modified:	09 Aug 2021 08:18
URI:	http://repository.unsri.ac.id/id/eprint/51944

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