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Jurnal INFOTEL
Published by Universitas Telkom
ISSN : 20853688     EISSN : 24600997     DOI : https://doi.org/10.20895/infotel.v15i2
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal INFOTEL is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto, Indonesia. Jurnal INFOTEL covers the field of informatics, telecommunication, and electronics. First published in 2009 for a printed version and published online in 2012. The aims of Jurnal INFOTEL are to disseminate research results and to improve the productivity of scientific publications. Jurnal INFOTEL is published quarterly in February, May, August, and November. Starting in 2018, Jurnal INFOTEL uses English as the primary language.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 13 Documents
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Search results for , issue "Vol 17 No 2 (2025): May" : 13 Documents clear
Load-Shedding Optimization Using Hybrid Grey Wolf - Whale Algorithm to Improve The Isolated Distribution Networks Sujono Sujono; Akhmad Musafa
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1336

Abstract

The integration of distributed generation allows the distribution network to operate in either on-grid or off-grid mode. In off-grid mode, the power supply from the main grid is interrupted, and distributed generation becomes the main source of power to meet the load's power demand. The absence of power supply from the main grid reduces the grid's ability to meet load power demand. The load power demand is larger than the distributed generation capacity, causing a power deficit in the network. This paper studies strategies for restoring power balance through optimal load shedding, taking into account the presence of priority loads that require power demand to be maintained and met. The optimization objective is to maximize the remaining load with an optimal composition so that the power loss is minimal. The load-shedding optimization uses a hybrid Grey Wolf Algorithm and Whale Optimization Algorithm (GW-WOA). The performance of GW-WOA is tested by load shedding optimization on a 118-bus IEEE radial distribution system integrated with 12 units of DG. The network loading factor variation consists of 80%, 100%, and 140% of the base load. Regarding all loading factors, the GW-WOA hybrid algorithm is superior to the standard GWO and WOA. The GW-WOA hybrid algorithm can converge faster to obtain the global optimal solution to realize power balance, overcome power deficit, maximize remaining load, and minimize power loss in the network. The GW-WOA hybrid algorithm has improved the performance of load-shedding optimization in isolated distribution networks with global optimal results and shorter iterations.
Implementation of Discrete Wavelet Transform and Xception for ECG Image Classification of Arrhythmic Heart Disease Patients Muhammad Irhamsyah; Melinda Melinda; Yunidar Yunidar; Ikram Muttaqin; Lailatul Qadri Zakaria
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1341

Abstract

The electrocardiogram (ECG) is one of the most important methods in the process of diagnosing heart disease. Visualizes the voltage and time relationship of the electrical activity of the heart. Cardiovascular or heart disease can be classified into several types, one of which is arrhythmia, a condition that involves changes in heartbeat rhythm, either too fast or too slow at rest. This study aims to develop a cardiac arrhythmia classification model using Deep Wavelet Transform (DWT) and Xception. It was evaluated on 2,200 spectrogram samples from the MIT-BIH dataset, containing normal and arrhythmia classes. The process compared epochs 30, 50, and 100 with learning rates of 0.001 and 0.0001 using cross-validation. Data were converted into spectrogram images for classification with Xception. The highest accuracy, 99.79%, was achieved at epoch 100 with a 0.0001 learning rate. Then, the highest precision occurs when the epoch is 50 with a learning rate of 0.001 and 0.0001, which is 100%. Lastly, Xception performed very well in the ECG image classification. This advantage demonstrates the ability of the model to recognize complex patterns in ECG data more effectively, increasing the reliability of arrhythmia detection. In addition, using DWT as a feature extraction technique allows better signal processing,which contributes to optimal results.
Metode Migrasi Lebah Madu Ratu untuk Meningkatkan Deteksi Fibrilasi Atrium dari Sinyal Detak Jantung Muhammad Hafiizh; Aripriharta Aripriharta; Ilham Ari Elbaith Zaeni
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1362

Abstract

Atrial Fibrillation (AF) is a common cardiac arrhythmia characterized by rapid and irregular electrical activity of the atrium. AF significantly increases the risk of ischemic stroke and mortality. With the increasing prevalence of cardiovascular risk factors, early detection of AF is crucial for effective intervention. Traditional electrocardiogram (ECG)-based detection methods face limitations, especially in asymptomatic patients or those with sporadic episodes of AF. This paper proposes a novel approach using the Queen Honey Bee Migration (QHBM) algorithm to detect AF from heartbeat signals. The dataset comprises both normal and AF heartbeat signals. The data undergoes preprocessing steps, including noise reduction and feature extraction. The system then classifies the signals using the QHBM algorithm. Key features such as heart rate variability (HRV), amplitude, and RR intervals are extracted for analysis. The QHBM algorithm achieved an accuracy of 95.2%, with a precision of 96.1%, a recall of 94%, and an F1 score of 95%. It outperformed traditional classifiers such as Random Forest, Support Vector Machine (SVM), and Naive Bayes across all performance metrics. In addition, QHBM demonstrated a superior ability to distinguish between normal sinus rhythm and AF, showing a significant improvement over the conventional method. Although the results are promising, challenges remain, including data imbalance and false positive and negative classifications. Oversampling techniques and further optimization of feature selection can enhance model performance. The QHBM algorithm presents a highly effective solution for automatic and real-time AF detection, offering a promising alternative to improve cardiac health monitoring systems.

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