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

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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.

Availability
Inventory Code Barcode Call Number Location Status
2107002501T51944T519442021Central Library (Referens)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T519442021
Publisher
Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xvii, 53 hlm,: ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
004.07
Content Type
Text
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Pemrosesan Data, Teknik Informatika
Prodi Teknik Informatika
Specific Detail Info
-
Statement of Responsibility
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No other version available

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