Skripsi

PENGARUH REDUKSI FITUR MENGGUNAKAN SVD PADA PENGKLASIFIKASIAN KNN

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Penilaian

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High-dimensional data is a data that has many attributes, one of them is internet traffic data. This research used heart disease data with 76 attributes. If the heart disease data is going to be classified, a dimensional reduction technique is needed, because conventional classification algorithms work better in handling low dimensional data. Dimension reduction techniques are classified into 2 types, feature selection and feature extraction. This study will compare the implementation of the Singular Value Decomposition (SVD) algorithm as a feature selection for KNN classification algorithm. The results obtained by ANOVA shows insignificant differences on the value of accuracy, precision, and recall. However, in terms of computation time, the combination of SVD and KNN is proven to be faster than the KNN itself.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2107003602	T432172	T432172021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T432172021

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2021

Collation

xx, 94 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Pengolahan Data Elektronis
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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