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PENERAPAN TEKNIK RANDOM OVERSAMPLING UNTUK MENGATASI IMBALANCED CLASS DATA PADA KLASIFIKASI TINGKAT KEBUGARAN TUBUH MANUSIA MENGGUNAKAN METODE K-NEAREST NEIGHBOR

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Penilaian

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Physical fitness is the body's ability to carry out physical activities without causing excessive fatigue, knowing a level of body fitness is useful for determining the right solution to overcome problems related to body fitness. A classification process with machine learning is needed to determine the level of body fitness, one method that is often used is the K-NN method. Classification using machine learning often has a problem with class imbalance that causes errors in classification, to overcome this problem a data balancing method is needed. The random oversampling technique is a data balancing technique by randomly adding minority class samples until the number of samples is equal to the majority class. The results obtained after applying random oversampling decreased the average accuracy by 4% and the average precision by 6%, but there was an increase in the average recall value by 4%. This research proves that the random oversampling technique can make the recall value higher.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000743	T87448	T874482023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T874482023

Publisher

: Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xviii, 110 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.707

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
Data dalam sistem-sistem komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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