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

SULISTIANA, MIRA and Yahdin, Sugandi and Desiani, Anita (2023) REDUKSI DIMENSI DENGAN PRINCIPAL COMPONENT ANALYSIS (PCA) PADA KLASIFIKASI POLA BEAT EKG PENYAKIT ARITMIA. Undergraduate thesis, Sriwijaya University.

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Abstract

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

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Reduksi Dimensi, PCA, EKG, Aritmia, Klasifikasi
Subjects:	Q Science > QA Mathematics > QA1-43 General
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Mira Sulistiana
Date Deposited:	31 Oct 2023 01:47
Last Modified:	31 Oct 2023 01:47
URI:	http://repository.unsri.ac.id/id/eprint/130174

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