Skripsi

REDUKSI DIMENSI DENGAN PRINCIPAL COMPONENT ANALYSIS (PCA) PADA KLASIFIKASI POLA BEAT EKG PENYAKIT ARITMIA

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Penilaian

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Dimentional reduction is a technique for reducing the number of features in a dataset. Principal Component Analysis (PCA) is a dimensional reduction method that can reduce the features in a dataset without eliminating important information in it. In this study, the aim is to obtain the results of dimension reduction as well as to know the performance results before and after data dimension reduction. In this study, PCA was used to reduce the dimensions of ECG beat data for arrhythmias and to speed up the classification process. The method used in this study is the PCA method to reduce large data dimensions into smaller dimension datasets so as to simplify the classification process. Measurement of performance results using the Naïve Bayes algorithm, KNN, and Decision Tree. There is an increase in performance results after the data dimensions are reduced in the Naïve Bayes algorithm, there is an increase in precision of 1%, recall of 3%, and F1-Score of 4%. In the KNN classification there is an increase in precision, recall, and F1-Score each of 0.5%. In the Decision Tree classification there is an increase in precision, recall, and F1-Score performance of 1% each. Based on the results obtained, it can be concluded that the use of PCA can improve the performance of the classification method on the ECG beat dataset for arrhythmias

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307006269	T130174	T1301742023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1301742023

Publisher

Inderalaya : Jurusan Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Sriwijaya., 2023

Collation

viii, 56 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

510.285 07

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Jurusan Matematika
Pengolahan dan Analisa Data di Bidang Matematika

Specific Detail Info

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Statement of Responsibility

SEPTA

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