Skripsi

ANALISIS EKSTRAKSI FITUR FREQUENCY DOMAIN UNTUK KLASIFIKASI ABNORMALITAS JANTUNG MENGGUNAKAN MACHINE LEARNING

---

Penilaian

---

Cardiac abnormalities are disorders in heart function that can be detected through electrocardiogram (ECG) signals. This research uses a frequency domain-based feature extraction method with Fast Fourier Transform (FFT), using ten features which then the data will be classified using machine learning algorithms, such as SVM, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN). Results show that Random Forest has the best performance with 100% accuracy on test data and 83% on validation data. Desicion Tree achieved 100% accuracy (test data) and 75% (validation data), KNN achieved 83% (test data) and 75% (validation data), while SVM only obtained 50% accuracy. The combination of feature extraction and appropriate algorithms proved effective in detecting cardiac abnormalities and can support a faster and more accurate diagnosis process.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507004243	T179014	T1790142025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1790142025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 98 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Sistem komputer

Specific Detail Info

-

Statement of Responsibility

MI

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment