Skripsi

PERBANDINGAN TEKNIK REDUKSI DIMENSI ANTARA ALGORITMA PRINCIPAL COMPONENT ANALYSIS DENGAN FUZZY ASSOCIATION RULE TERHADAP HASIL PENGKLASTERAN

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Penilaian

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Clustering is the process of grouping data into several groups, where each member of the group has big similarities and disimilarities to other member of the group. In clustering, conventional algorithm works well in handling low-dimensional data, therefore to improve the quality of text clustering results, dimensional reduction technique is required. Dimensional reduction techniques are classified into 2 types, feature selection and feature extraction. This study will compare the application of the Principal Component Analysis (PCA) and Fuzzy Association Rule as a feature extraction technique for k-Means Clustering algorithm. The results obtained by the combination of Fuzzy Association Rule and k-Means improve the performance of text clustering by 22,04%, while combination of PCA and k-Means just improve the performance of text clustering by 18,05%.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2007000083	T38720	T387202020	Central Library (REFERENSI)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T387202020

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2020

Collation

xviii, VI-2 hlm, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.360 7

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Sistem informasi
Teknik Informatika

Specific Detail Info

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Statement of Responsibility

NO

Other version/related

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