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Jusikom : Jurnal Sistem Komputer Musirawas
Published by Universitas Bina Insan Lubuklinggau
ISSN : 25411896     EISSN : 26148714     DOI : https://doi.org/10.32767/jusikom.v9i1
Core Subject : Science,
Computer Science & IT
JUSIKOM is a place of information in the form of research results, literature studies, ideas, application of theory and critical analysis studies in the fields of research in the fields of Computer Systems, Computer Science, and Electronics. Focus and Scope: Embedded system, Intelligent control system, Software engineering, Computer network, Mobile computing, Artificial Intelligent, Internet of Things, and Information system.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 221 Documents
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DETEKSI KADAR HBA1C BERBASIS SINYAL PHOTOPLETHYSMOGRAPHY (PPG) Mahfudhoh, Eny; Anggraeni, Dinda Wahyu; Adilla, Axl
Jusikom : Jurnal Sistem Komputer Musirawas Vol 10 No 2 (2025): Jurnal Sistem Komputer Musirawas DESEMBER
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v10i2.2858

Abstract

Diabetes is a chronic disease with a high prevalence in Indonesia, requiring routine blood glucose monitorin1. However, the standard method for measuring Glycated Hemoglobin (HbA1c) is invasive, painful, and costly. This study aims to summarize and discuss the non-invasive estimation of HbA1c levels using Photoplethysmography (PPG) signals. PPG, a non-invasive optical technique, detects microvascular blood volume changes. Its pulse wave morphology is affected by biomechanical and hemodynamic alterations due to HbA1c accumulation, such as increased arterial stiffness. Various studies have explored the extraction of PPG signal features (statistical, physiological, and AC/DC ratio), which are then processed using machine learning and deep learning algorithms like 1D-CNN, XGBoost, Random Forest, and QSVM. The results demonstrate promising performance, with some models achieving Pearson correlation coefficients up to R = 0.96 and a clinical accuracy of 100% estimation points falling within Zone A of the Clarke Grid Analysis (CGA). The non-invasive approach based on PPG and artificial intelligence offers an accurate, fast, and comfortable solution for HbA1c monitoring, marking a crucial advancement in diabetes management.

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