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Bulletin of Electrical Engineering and Informatics
Published by Universitas Ahmad Dahlan
ISSN : -     EISSN : -     DOI : -
Core Subject : Engineering,
Electrical & Electronics Engineering
Bulletin of Electrical Engineering and Informatics (Buletin Teknik Elektro dan Informatika) ISSN: 2089-3191, e-ISSN: 2302-9285 is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world. The journal publishes original papers in the field of electrical, computer and informatics engineering.
Arjuna Subject : -
Articles 2,901 Documents
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IoT-based health information system using MitApp for abnormal electrocardiogram signal monitoring Utomo, Bedjo; Triwiyanto, Triwiyanto; Luthfiyah, Sari; Caesarendra, Wahyu; Anant Athavale, Vijay
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5205

Abstract

Information systems are currently developing very rapidly, and this is inseparable from the role of internet of things (IoT) technology, especially in the world of telemedicine. MitApp is an open-source application that can be used to monitor electrocardiogram (ECG) signals in real-time. The aim of this study is to develop an IoT-based ECG signal monitoring system that utilizes the MitApp application to detect abnormal ECG signals that are characterized by symptoms of cardiac arrhythmias. To process ECG signal data obtained from lead electrode results, the research method utilizes Arduino Uno as a microcontroller. The result is then displayed on the thin film transistor (TFT) layer using the Nextion module. The ESP32 module is used as a Wi-Fi module to send data to the MitApp app on a smartphone. The results showed that the results of the comparison test of ECG signal module data with ECG simulator tools with beats per minute values of 60, 80, 100, 120, and 140 obtained an error rate of 0.05. Based on these results, there is potential to develop this feature and integrate the system with the patient management system to improve the effectiveness of remote monitoring.
An image analysis technique for wheat head count detection using machine learning Kalluri, Ramadevi; Selvaraj, Prabha
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7255

Abstract

Deep learning (DL) techniques have significantly enhanced the potential for wheat head detection in recent times. The different development phases of canopy, genotype, wheat heads, and wheat head orientation provide considerable obstacles. The overlapping density of wheat heads and wind- induced picture blurring complicate wheat head recognition. This study describes an effective wheat head detection and counting method. Due to its high throughput in agricultural field analysis, remote sensing phenotyping has grown in popularity. Applying DL methods for image processing and other technological advancements has increased the scope for the quantitative evaluation of various crop traits. The ability to detect and characterize wheat heads in the industry is an important part of the wheat breeding process for selecting high-yielding cultivars. The proposed method uses the Mask region-based convolutional neural network (RCNN) framework to detect and classify the wheat ears. The complete detection task is done in three steps: region proposal generation, region of interest alignment, and mask generation. The global wheat head detection (GWHD) dataset is used for the experimental analysis of the dataset. The proposed method achieved an accuracy of 95.11% on the GWHD dataset, demonstrating its effectiveness in wheat head detection and classification tasks.
Addressing the complexities of postoperative brain MRI cavity segmentation–a comprehensive review P, Sobha Xavier; P K, Sathish; G, Raju
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7930

Abstract

Postoperative brain magnetic resonance images (MRI) is pivotal for evaluating tumor resection and monitoring post-surgical changes. The segmentation of surgical cavities in these images poses challenges due to artifacts, tissue reorganization, and heterogeneous appearances. This study explores challenges and advancements in postoperative brain MRI segmentation, examining publicly accessible datasets and the efficacy of various deep learning models. The analysis focuses on different U-Net models (U-Net, V-Net, ResU-Net, attention U-Net, dense U-Net, and dilated U-Net) using the EPISURG dataset. The training dice scores are as follows: U-Net 0.8150, attention U-Net 0.8534, V-Net 0.7602, ResU-Net 0.7945, dense U-Net 0.83, dilated U-Net 0.80. The study thoroughly assesses existing postoperative cavity segmentation models and proposes a fine-tuning approach to enhance the performance further, particularly for the best-performing model, attention U-Net. This fine-tuning involves introducing dilated convolutions and residual connections to the existing attention U-Net model, resulting in improved results. These improvements underscore the necessity for ongoing research to select and adapt efficient models, retrain specific layers with a comprehensive collection of post-operative images, and fine-tune model parameters to enhance feature extraction during the encoding phase.
Solar power forecasting model as a renewable generation source on virtual power plants Suwarno, Suwarno; Pinayungan, Doni
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5870

Abstract

This paper describes modeling solar power generation as a renewable energy generator by simulating the analytical approach mean absolute error and root mean square error (MAE and RMSE). This research estimates the error referring to long short-term memory (LSTM) network learning. Related to this, the Indonesian government is currently actively developing solar power plants without ignoring the surrounding environment. The integration of solar power sources without accurate power prediction can hinder the work of the grid and the use of new and renewable generation sources. To overcome this, virtual power plant modeling can be a solution to minimize prediction errors. This study proposes a method for on-site virtual solar power plant efficiency with a research approach using two models, namely RMSE and MAE to account for prediction uncertainty from additional information on power plants using virtual solar power plants. A prediction strategy verified against the output power of photovoltaic (PV) modules and a set based on data from meteorological stations used to simulate the virtual power plants (VPP) model. This forecast prediction refers to the LSTM network and provides forecast errors with other learning methods, where the approach simulated with 12.36% and 11.85% accuracy for MAE and RMSE, respectively.
Best Agile method selection approach at workplace Merzouk, Soukaina; Jabir, Brahim; Marzak, Abdelaziz; Sael, Nawal
Bulletin of Electrical Engineering and Informatics Vol 13, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i3.5782

Abstract

Selecting the most suitable agile software development method is a challenging task due to the variety of available methods, each with its strengths and weaknesses. To achieve project goals effectively, factors such as project needs, team size, complexity, and customer involvement should be carefully evaluated. Choosing the appropriate agile method is crucial for achieving high client satisfaction and effective team management, but it can be a challenging task for project managers and higher-level management officials.This paper presents a solution aiming to help them in selecting the most suitable software development method for their project. In this regard, this solution includes a pre-project management approach model and a decision tree that considers the unique requirements of the project. In the proposed solution results, Scrum was found to be suitable for both small and large projects, on the condition that roles and responsibilities are clearly defined and that the approach is people-centric. Furthermore, high-risk mitigation measures should be added for small projects. To facilitate the use of our model, a software application has been developed which implements the decision-making tree.
Stability analysis of power system under n-1 contingency condition Baleboina, Guru Mohan; Rudramoorthy, Mageshvaran
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6133

Abstract

Several voltage stability indices (VSIs) have been developed to assess the potential for voltage collapse. However, certain indexes are computationally costly. Meanwhile, some have been noted to underperform across various conditions. This work proposes a novel line index called the super voltage stability index (SVSI) to calculate the system's voltage stability margin (VSM). The suggested approach is based on the transmission system's two bus systems. The reactive power loss and N-1 contingency conditions to voltage sensitivity is a unique calculation approach used in this study to identify voltage instability. Day to day, the demand for electric power is being increased due to incessant increments in technology and population growth. Therefore, the power system networks are under pressure. The operational conditions of transmission system networks are affected at this point, which may result in voltage collapse. Regular monitoring of power supply is essential to avert voltage collapse. The effectiveness of the suggested index has been assessed using the IEEE 5 and 30-bus systems across diverse operating scenarios, including variations in active and reactive power loading as well as single line losses. The findings indicate that SVSI provides a more reliable indication of the proximity to voltage collapse when compared to conventional line VSIs.
Evaluation of the performance of the vehicular ad hoc network protocols in the case of V2I and EV2I communications Mohammed, Dania; Mansor, Muhamad; Hock, Goh Chin
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5516

Abstract

Vehicular ad hoc network (VANET) is an intelligent technology that enables efficient communication, secure data transmission, and traffic management. The purpose of routing protocols in the VANET network is to route data between vehicles (V2V) and vehicles-to-infrastructure (V2I). Recently, researchers have shown interest in designing effective routing protocols for the VANET network, as not all existing protocols are suitable for all traffic scenarios. Electric vehicles (EVs) are increasingly being adopted and integrated into intelligent transportation systems (ITS). Developed countries are actively promoting sustainable transportation solutions to increase energy efficiency and reduce carbon emissions. Therefore, this research presents an EV charging station (CS) management scheme based on communication between EVs and RSUs, with performance evaluation simulated using VANET network protocols. In this study, the G-MDORA, MDORA, and geographical routing protocol (GRP) protocols were modified to accommodate V2I communication, and RSUs were distributed along the roadmap. Additionally, a scheme for managing electric vehicle CS was presented, focusing on the communication between electric vehicles and RSUs in the EV2I context. Performance was evaluated using the G-MDORA, GRP, and MDORA protocols, considering factors such as throughput, communication overhead, packet delivery ratio, and end to end delay.
Pattern analysis on Aquilaria Malaccensis using machine learning Hasnu Al-Hadi, Anis Hazirah 'Izzati; Mohd Huzir, Siti Mariatul Hazwa; Zaidi, Amir Hussairi; Ismail, Nurlaila; Mohd Yusoff, Zakiah; Haron, Mohamad Hushnie; Taib, Mohd Nasir
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5562

Abstract

Aquilaria Malaccensis was found to generate agarwood. Because of its multiple benefits, agarwood essential oil, sometimes known as “black gold” is highly regarded universally. There is currently no accepted method for classifying various grades of agarwood essential oil. Due to the fact that the agarwood essential oil is assessed using a human sensory panel, the existing grading method is ineffective. Since different people may have different viewpoints on how to grade agarwood essential oil, it is not practical to apply the method universally. Several innovative methods for determining the classification of agarwood essential oil have been proposed and put into practise as a result of advanced technology. The study has constructed a pattern analysis on different grades of agarwood essential oil using 2D scatter plot. The results successfully indicate the scatter plots are scattered groupedly.
Optimized k-nearest neighbours classifier based prediction of epileptic seizures Jagath Prasad, Himayavardhini; Marjorie S., Roji
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6598

Abstract

Epileptic seizure is an unstable condition of the brain that cause severe mental disorder and can be fatal if not properly diagnosed at an early stage. Electroencephalogram (EEG) plays a major role in early diagnosis of epileptic seizures. The volume of medical databases is enormous. Classification may become less accurate if the dataset contains redundant and irrelevant attributes. To reduce the mortality rate due to epilepsy, a decision support system that can assist medical professionals in taking immediate precautionary measures prior to reaching the critical condition is required. In this work, k-nearest neighbours (KNN) classifier algorithm is optimised using genetic algorithm for effective classification and faster prediction to meet this requirement. Genetic algorithms search for optimal solutions in complex and large environments. Results are compared with other machine learning models such as support vector machine (SVM), KNN, decision tree classifier, and random forest. With optimization using genetic algorithm KNN was able to achieve an enhancement in accuracy at lower training and testing times. It was observed that the accuracy offered by optimized KNN was 92%. Random forest classifiers showed minimum complexity and KNN algorithm provided faster performance with better accuracy.
Energy saving performance analysis for future fifth generation millimetre-wave cellular networks Anwar Apandi, Nur Ilyana; Muhammad, Nor Aishah
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.5973

Abstract

The deployment of fifth generation (5G) millimetre-wave (mmWave) base stations (BSs) will consume more energy over time due to the limited time available, despite the increasing interest in developing 5G mmWave wireless communication technology. Constructing 5G mmWave cellular network infrastructure can improve energy efficiency, which is a challenge to implement in heterogeneous networks. This paper presents analytical frameworks for monitoring the effectiveness of 5G mmWave cellular networks. Based on the state management of BS, a system model for 2-tier heterogeneous networks is developed, and particle swarm optimization (PSO) is then used to compute the total energy consumption of the heterogeneous networks. Energy consumption was compared and analysed by leveraging state switching and the aggregate delay for three methods: fundamental separation, conventional separation, and a proposed energy-saving method that introduced a sleep state. Simulation shows that the proposed energy-saving method, which is a combination of conventional separation approaches, has the lowest total energy consumption and offers a 9% reduction compared to other related works. The results validate the accuracy of the power usage used in the 5G mmWave cellular network of the proposed method.

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