PENERAPAN REDUKSI DIMENSI DENGAN LINEAR DISCRIMINANT ANALYSIS PADA KLASIFIKASI PENYAKIT ARITMIA

MAHIDA, ALGA and Desiani, Anita (2023) PENERAPAN REDUKSI DIMENSI DENGAN LINEAR DISCRIMINANT ANALYSIS PADA KLASIFIKASI PENYAKIT ARITMIA. Undergraduate thesis, Sriwijaya University.

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Abstract

Arrhythmia disease is a serious and potentially fatal condition, so detecting this disease early has great significance. However, the dataset in the form of electrocardiogram (ECG) used in the analysis of arrhythmia disease consists of attributes, namely 252 free attributes and 1 label. Thus, 252 signal points are taken which means 1 signals points are taken which means 1 signal has 252 attributes. The more attributes, the more memory is required, but computers have limited memory. To overcome this problem, dimensionality reduction is performed to reduce attributes without losing important information. Linear Discriminant Analysis (LDA) was chosen as one of the statistical feature extraction methods used to reduce the high dimensionality of data. This study aims to apply LDA in reducing the dimensionality of the MIT-BIH arrhythmia classification. The results of this attributes to 10 attributes and the Naive Bayes algorithm obtained an increase in accuracy of 21%, precision of 14%, recall of 21%, and f1-score of 28%. The KNN algorithm obtained an increase in accuracy of 3%, precision 2%, recall 3%, and f1-score 2%. The SVM algorithm obtained an increase in accuracy of 3%, precision of 3%, recall 4%, and f1-score 4%. The arrhythmia disease dataset experienced a significant increase ini precision, recall, and f1-score accuracy by 9%, 6%, 9%, and 11%, respectively. Based on the results ibtained, it can be concluded that dimension reduction with LDA improves classification performance on the MIT-BIH arrhythmia disease dataset.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Aritmia, Reduksi Dimensi, LDA, Klasifikasi, EKG
Subjects:	Q Science > QA Mathematics > QA1-939 Mathematics > QA37.3.1.64 Applied Mathematics
Divisions:	08-Faculty of Mathematics and Natural Science > 44201-Mathematics (S1)
Depositing User:	Alga Mahida
Date Deposited:	07 Nov 2023 00:58
Last Modified:	07 Nov 2023 00:58
URI:	http://repository.unsri.ac.id/id/eprint/130382

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