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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 222 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.
OPTIMALISASI SMART AGRICULTURE MELALUI PREDIKSI HARGA SAYURAN BERBASIS DEEP LEARNING SEBAGAI UPAYA MENDUKUNG KETAHANAN PANGAN NASIONAL rahmadayanti, fitria; muntari, siti
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.v10i2.2872

Abstract

The agricultural sector plays a vital role in supporting national food security. However, farmers in many regions, including Pagar Alam—one of the main vegetable production centers in South Sumatra—continue to face significant challenges due to unpredictable price fluctuations. This instability makes it difficult for farmers to determine the optimal timing for planting, harvesting, and distributing their produce, which often results in economic losses and inefficiencies within the supply chain. Such conditions directly impact farmers’ welfare and the stability of market supply. This study aims to identify patterns of vegetable price fluctuations through data analysis and the development of a prediction model using a deep learning approach, specifically the Long Short-Term Memory (LSTM) algorithm. Evaluation of the model’s performance is conducted to determine the best predictive model based on accuracy and result stability. The findings are expected to provide data-driven policy recommendations to support Smart Agriculture initiatives and strengthen food security at both local and national levels.The research adopts the CRISP-DM framework, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The expected outcome of this study is the development of a predictive model that can offer valuable insights and recommendations to stakeholders, ultimately contributing to the improvement of farmers’ welfare.

Page 23 of 23 | Total Record : 222

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