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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 73 Documents
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Search results for , issue "Vol 13, No 2: April 2024" : 73 Documents clear
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.
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.
A machine learning-based computer model for the assessment of tsunami impact on built-up indices using 2A Sentinel imageries Joko Prasetyo, Sri Yulianto; Simanjuntak, Bistok Hasiholan; Susatyo, Yeremia Alfa; Sulistyo, Wiwin
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.v13i2.5910

Abstract

This study aims to build a computer model to detect built-up land in the identified tsunami hazard zone based on Sentinel 2A imagery using the normalized built up area index (NBI), urban index (UI), normalize difference build-up index (NDBI), a modified built-up index (MBI), index-based builtup index (IBI) algorithms, optimized with machine learning Random Forest (RF) and extreme gradient boosting (XGboost) algorithms and the spatial patterns are predicted using the ordinary kriging (OK) method. Testing of the accuracy of the classification and optimization results was performed using the Kohen Kappa and overall accuracy functions. The results of the study show that a built-up land consisting of open land and water, settlements, industry areas, and agriculture and tourism areas can be identified using the parameters of built-up indices. The accuracy testings that were performed using overall accuracy and Kohen Kappa methods show that classification and prediction are highly accurate using XGboost machine learning, namely 91%. This study produces a novelty of finding, namely a computer model to detect and predict the spatial distribution of built-up land in 4 scales, i.e., very low, low, high, and very high based on NBI, UI, NDBI, MBI, IBI data extracted from Sentinel 2A imagery.
Simplifying the electronic wedge brake system model through model order reduction techniques Che Hasan, Mohd Hanif; Hassan, Mohd Khair; Ahmad, Fauzi; Marhaban, Mohammad Hamiruce; Haris, Sharil Izwan; Arasteh, Ehsan
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.v13i2.5815

Abstract

The electronic wedge brake (EWB) uses self-reinforcement principles to optimise stopping power, but its mathematical model has various actuation angles and system dynamics making controller design complex and computationally burdensome. Therefore, the model order reduction (MOR) is made based on three factors that may have a negligible influence on the EWB system: the motor inductance, lead screw axial damping, and wedge mass. Six reduced order model (ROM) types were proposed when one, two, or all factors were ignored. The ROM accuracy was analysed using the frequency and time domain. The percentage of root means square error (RMSE) response value between the EWB benchmark model, and the predicted response based on the ROM was found to be less than 2%, with ROM size reduced from 5 to 2 orders. It guarantees that the new ROM series will be useful for simpler EWB controller design. The proposed ROM simplifies the original model drastically while retaining accuracy at an adequate level. Even though the simplest EWB model is a 2nd  order linear system, the best ROM vary depending on EWB design parameters.
Mathematics for 2D face recognition from real time image data set using deep learning techniques G. N., Ambika; Suresh, Yeresime
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.v13i2.5424

Abstract

The recognition of human faces poses a complex challenge within the domains of computer vision and artificial intelligence. Emotions play a pivotal role in human interaction, serving as a primary means of communication. This manuscript aims to develop a robust recommendation system capable of identifying individual faces from rasterized images, encompassing features such as eyes, nose, cheeks, lips, forehead, and chin. Human faces exhibit a wide array of emotions, with some emotions, including anger, sadness, happiness, surprise, fear, disgust, and neutrality, being universally recognizable. To achieve this objective, deep learning techniques are leveraged to detect objects containing human faces. Every human face exhibits common characteristics known as Haar features, which are employed to extract feature values from images containing multiple elements. The process is executed through three distinct stages, starting with the initial image and involving calculations. Real-time images from popular social media platforms like Facebook are employed as the dataset for this endeavor. The utilization of deep learning techniques offers superior results, owing to their computational demands and intricate design when compared to classical computer vision methods using OpenCV. The implementation of deep learning is carried out using PyTorch, further enhancing the precision and efficiency of face recognition.
A comprehensive survey on several fire management approaches in wireless sensor networks Rajendran, Swetha; Chenniappan, Navaneethan
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.v13i2.5833

Abstract

The majority of the fires are activated through environmental reasons although a minority of them are self-activated. To detect fires several safety systems were introduced. There are wired systems, cameras, satellite systems, and bluetooth feasible to provide a complete image of the world but after a long search period. These systems are not perfect since it prevents fire from finding just at the time, the fire initiates. But, recent technological development in wireless sensor networks (WSN) has spread out its fire detection application. A comprehensive survey on several fire management approaches in WSN propose to discuss various fire detection approaches like early fire detection, energy efficient fire detection, mobile agent-based fire detection, unmanned aerial vehicle (UAV)-based fire detection, threshold-based fire detection, machine learning based fire detection and secure fire detection approaches. Moreover, the comprehensive tabular study of the fire management technique is given that will assist in the suitable selection of approaches to be applied for the detection of fire. Furthermore, WSN uses the clustering method to minimize redundant dataandsecure fire detection approaches collect authenticated data related to fire detection. Early fire detection approaches detects the fire early. Machine learning algorithm detects the fire efficiently.
Squirrel search method for deep learning-based anomaly identification in videos Malphedwar, Laxmikant; Rajesh Kumar, Thevasigamani
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.v13i2.5933

Abstract

The monitoring of human behavior and traffic surveillance in various locations has become increasingly important in recent years. However, identifying abnormal activity in real-world settings is a challenging task due to the many different types of worrisome and abnormal actions, including theft, violence, and accidents. To address this issue, this paper proposes a new framework for deep learning-based anomaly identification in videos using the squirrel search algorithm and bidirectional long short-term memory (BiLSTM). The proposed method combines the squirrel search algorithm, an optimization technique inspired by nature, with BiLSTM for anomaly recognition. The framework uses the knowledge gained from a sequence of frames to categorize the video as either typical or abnormal. The proposed method was exhaustively tested in several benchmark datasets for anomaly detection to confirm its functionality in challenging surveillance circumstances. The results show that the proposed framework outperforms existing methods in terms of area under curve (AUC) values, with a test set AUC score of 93.1%. The paper also discusses the importance of feature selection and the benefits of using BiLSTM over traditional unidirectional long short-term memory (LSTM) models for anomaly detection in videos. Overall, the proposed framework provides a highly precise computerization of the system, making it an effective tool for identifying abnormal human behavior in surveillance footage.
Bidirectional recommendation in HR analytics through text summarization Arandi, Channabasamma; Yeresime, Suresh
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.v13i2.5650

Abstract

For over a decade, online job portals have been providing their services to both job seekers and employers in search of hiring opportunities. Because of the high demand for recruitment, it is insufficient to use conventional hiring methods to find a suitable candidate to fill the position. Validating resumes online is challenging due to the potential for manual errors, making the process inherently risky. The bidirectional method comprises named entity recognition (NER) for extracting the required resumes for recruiters. Cosine similarity shows the match percentage of resumes for the job requirements and vice versa. In an attempt to tackle an issue of unregistered words, a solution called decoder attention with pointer network (DA-PN) has been introduced. This method incorporates the use of coverage mechanism to prevent word repetition through generated text summary. DA-PN+Cover method with mixed learning objective (MLO) (DA-PN+Cover+MLO) is utilized for protecting grow of increasing faults in generated text summary. Performance of proposed method is estimated using evaluation indicator recall oriented understudy for gisting evaluation (ROUGE) and attains an average of 27.47 which is comparatively higher than existing methods.
Optimized extreme learning machine using genetic algorithm for short-term wind power prediction Mansoury, Ibtissame; El Bourakadi, Dounia; Yahyaouy, Ali; Boumhidi, Jaouad
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.v13i2.6476

Abstract

Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.
Real-time monitoring tool for heart rate and oxygen saturation in young adults Fatimah Abdul Razak, Siti; Jia Wee, Yap; Yogarayan, Sumendra; Noor Masidayu Sayed Ismail, Sharifah; Fikri Azli Abdullah, Mohd
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.v13i2.6371

Abstract

Health monitoring is crucial to maintain optimal well-being, especially for young adults. Wearable sensors have become popular for collecting healthcare data, but there are concerns regarding their reliability and safety, particularly with wireless sensors that use radio-frequency (RF) based devices. Researchers have proposed real-time monitoring systems for measuring heart rate beats per minute (BPM) and blood oxygen saturation (SpO2) saturation levels, but more studies are needed to determine the accuracy and user acceptance of these tools among young adults. To address these concerns, this study proposes a real-time monitoring tool that incorporates MAX 30100 sensors to collect heart rate BPM and SpO2 data. The collected data is then connected to a visualization platform, i.e., InfluxDB and Grafana, to provide valuable insights of the body’s physiological state. By testing the feasibility and usability of the tool, we found motivating differences in resting heart rates and changes in heart rate after activity between male and female participants. By developing this real-time monitoring tool and investigating gender-specific differences in heart rate and activity-induced changes, our study contributes to the advancement of health monitoring technologies for young adults, ultimately promoting personalized healthcare and well-being.

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