Skripsi

OPTIMASI NILAI KLASTER PADA ALGORITMA K-MEANS MENGGUNAKAN ALGORITMA FIREFLY

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Penilaian

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K-Means is one of the most commonly used clustering algorithms for grouping data with similarities within a cluster. However, there are limitations in K-Means clustering, such as clustering results being initialized based on random centroid points and the number of clusters used. To improve the performance of the K-Means algorithm, the Firefly Algorithm is used for clustering optimization. The Firefly Algorithm offers flexibility in parameter determination and can deliver excellent performance. In the testing phase, the best optimization values with the Firefly Algorithm were obtained with the number of iterations = 60, α = 0.1, β_0 = 0.1, and γ = 0.01. This study shows that optimizing the Firefly Algorithm for K-Means clustering can improve clustering results, using the Silhouette Score as a benchmark. The closer the Silhouette Score is to one, the better the clustering result. The Silhouette Score for K-Means was 0.381, while the result for K-Means clustering optimized with the Firefly Algorithm was 0.431.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2407004274	T150630	T1506302024	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1506302024

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2024

Collation

xiii, V-5 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.310 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Clustering
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

KA

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