Prediksi Kejadian Vaskular pada Pasien Penyakit Jantung dengan Analisa HRV menggunakan Metode FACF

Akbar, Alvin Tarisa (2025) Prediksi Kejadian Vaskular pada Pasien Penyakit Jantung dengan Analisa HRV menggunakan Metode FACF. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penyakit jantung adalah penyebab kematian utama dunia dan memerlukan pemantauan terus-menerus yang mahal. Variabilitas denyut jantung (HRV) berbentuk time series telah digunakan sebagai indikator kesehatan. Metode Fragmented Autocorrelation Function (FACF) terbukti efektif untuk analisis time series ini. Penelitian ini menggunakan FACF pada data ECG untuk menjadi variabel yang membantu pemantauan pasien jantung dan membandingkannya dengan standar HRV menggunakan metode klaster dan klasifikasi. Hasil dari penelitian ini FACF mencapai akurasi maksimal 78% dan recall 100%, sementara standar HRV hanya 80% dan 88%. Meski akurasi FACF sedikit lebih rendah, akan tetapi memiliki nilai recall yang lebih tinggi, menunjukkan potensinya sebagai alternatif yang lebih baik dibanding standar HRV untuk analisis kondisi jantung.
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Heart disease is the leading cause of death worldwide and requires continuous monitoring which is expensive. Heart rate variability (HRV) in the form of time series has been used as an indicator of health. The Fragmented Autocorrelation Function (FACF) method has been proven effective for this time series analysis. This study uses FACF on ECG data to be a variable that helps monitor heart patients and compares it with HRV standards using cluster and classification methods. The results of this study FACF achieved a maximum accuracy of 78% and 100% recall, while HRV standards were only 80% and 88%. Although the accuracy of FACF is slightly lower, it has a higher recall value, indicating its potential as a better alternative than HRV standards for analyzing heart conditions.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	FACF, HRV, Time Series
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T57.5 Data Processing
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 59101-(S2) Master Thesis
Depositing User:	Alvin Tarisa Akbar
Date Deposited:	25 Jul 2025 06:57
Last Modified:	25 Jul 2025 06:57
URI:	http://repository.its.ac.id/id/eprint/121457

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