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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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DEMNET NeuroDeep: Alzheimer detection using electroencephalogram and deep learning M. Joshi, Vaishali; P. Dandavate, Prajkta; Rashmi, R.; R. Shinde, Gitanjali; D. Kulkarni, Deepthi; Mirajkar, Riddhi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8163

Abstract

Alzheimer’s disease (AD) stands out as the most prevalent neurological brain disorder, and its diagnosis relies on various laboratory techniques. The electroencephalogram (EEG) emerges as a valuable tool for identifying AD, offering a quick, cost-effective, and readily accessible means of detecting early-stage dementia. Detecting AD in its early stages is crucial, as early intervention yields more successful outcomes and entails fewer risks than treating the disease at a later stage. The objective of this research is to create an advanced diagnosis system for AD using machine learning (ML) and EEG data. The proposed system utilizes a multilayer perceptron (MLP) and a deep neural network with bidirectional long short-term memory (BiLSTM) as the classifier. The feature extraction process involves incorporating Hjorth parameters, power spectral density (PSD), differential asymmetry (DASM), and differential entropy (DE). The BiLSTM classifier, particularly when combined with DE, exhibits outstanding performance with an accuracy of 97.27%. This amalgamation of DE and the deep neural network surpasses current state-of-the-art techniques, underscoring the substantial potential of this approach for precise and advanced diagnosis of AD.
Enhancing detection of zero-day phishing email attacks in the Indonesian language using deep learning algorithms Roesmiatun Purnamadewi, Yasinta; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8759

Abstract

Email phishing is a manipulative technique aimed at compromising information security and user privacy. To overcome the limitations of traditional detection methods, such as blacklists, this research proposes a phishing detection model that leverages natural language processing (NLP) and deep learning technologies to analyze Indonesian email headers. The primary objective is to more efficiently detect zero-day phishing attacks by focusing on the unique linguistic and cultural context of the Indonesian language. This enables the development of models capable of recognizing phishing attack patterns that differ from those in other language contexts. Four models are tested, combining Indonesian bidirectional encoder representation of transformers (IndoBERT) and FastText feature extraction techniques with convolutional neural network (CNN) and long short-term memory (LSTM) deep learning algorithms. The results indicate that the combination of FastText and CNN achieved the highest performance in accuracy, precision, and F1-score metrics, each at 98.4375%. Meanwhile, the FastText model with LSTM showed the best performance in recall, with a score of 98.9583%. The research suggests exploring deeper into email content or integrating analysis between headers and email content in future studies to further improve accuracy and effectiveness in phishing email detection.
Identification and validation of factors affecting the success of smart village services Hadian, Nur; Purwanto, Purwanto; Wibowo, Adi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8022

Abstract

This study aims to explore and validate the factors that influence the performance and effectiveness of smart village services. Smart villages have become a focus for improving the quality of life of rural communities in the era of digital technology. However, there is a lack of methods to measure and evaluate the effectiveness of smart villages. We propose a holistic framework to measure and evaluate the effectiveness of smart services in smart villages. In this study, factors that influence the success of smart village effectiveness are identified. How effective the smart village services are can be understood using the information system success model approach by DeLone and McLean. This framework is expected to provide a better understanding of the effectiveness of smart village services so that people are willing to adopt the smart village service concept. In addition, this model can also be used as decision-making support for stakeholders and is expected to improve the quality of life of rural communities in a sustainable manner.
Global research trends in building-integrated photovoltaics: a bibliometric analysis (1971-2022) Tambunan, Handrea Bernando; Digwijaya, Wigas; Nurfanani, Achmad
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7453

Abstract

The number of academic publications in the building-integrated photovoltaics (BIPV) field has rapidly grown. Most published articles focus on a specific topic, such as mathematical model, solar architecture design, photovoltaic effect, solar cell, grid-connected, efficiency, performance assessment, economic analysis, optimization, and others with broader focus areas. This work focuses on BIPV research with bibliometric analysis through documents, cited references, authors, affiliations, countries, funding sponsors, sources, words, and conceptual structure based on the Scopus-indexed database between 1971 and 2022. The result shows that BIPV research constantly grows annually with strong collaboration authorship. China is the most relevant country with the top affiliation and funding sponsor to support the BIPV research. The terms conjugated polymers, photovoltaic properties, and organic polymer are identified as niche themes. On the other hand, the terms of conversion efficiency, perovskite, photovoltaic devices, solar cells, efficiency, and photoelectrochemical cell clusters are emerging themes. In the future, BIPV research will move towards microgrids, energy, performance, energy management systems, and energy efficiency issues. The finding will also provide researchers and organizations with a comprehensive understanding of BIPV research areas and new directions for future research.
Implementation of radio frequency identification technology for a secure and intelligent shopping cart N. Arinze, Stella; U. Okafor, Patrick; R. Obi, Emenike; O. Nwajana, Augustine
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8243

Abstract

Shopping at supermarkets has become a daily activity in urban areas of Enugu, Nigeria. However, there is always a huge rush in most mega supermarkets during times of discount offers, weekends and holidays resulting in long queues due to the barcode billing process. This research proposes a way of reducing the time spent at the billing counter using a radio frequency identification (RFID) smart-based shopping cart. To achieve this objective, an RFID tag, RFID reader, Arduino microcontroller and light-emitting diode (LED) display were used to develop a smart shopping trolley. RFID tag was placed on each of the eight products displayed for sale. RFID reader reads all the products that were placed on the cart and the details of the product such as the name, quantity, cost, and total cost was displayed on the LED. The smart shopping trolley system also incorporates an alarm system that triggers off when the RFID tag is removed from a product to avoid shoplifting and make the system secure for the owners of the supermarket. The result showed that the billing of the products was done directly from the smart shopping cart. The system was compared with the conventional barcode system and was found to overcome the limitations of time-consuming billing procedures.
The use of generative adversarial network as a domain adaptation method for cross-corpus speech emotion recognition Farhan Fadhil, Muhammad; Zahra, Amalia
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8339

Abstract

The research of speech emotion recognition (SER) is growing rapidly. However, SER still faces a cross-corpus SER problem which is performance degradation when a single SER model is tested in different domains. This study shows the impact of implementing a generative adversarial network (GAN) model for adapting speech data from different domains and performs emotion classification from the speech features using a 1D convolutional neural network (CNN) model. The results of this study found that the domain adaptation approach using a GAN model could improve the accuracy of emotion classification in speech data from 2 different domain such as the ryerson audio-visual database of emotional speech and song (RAVDESS) speech corpus and the EMO-DB speech corpus ranging from 10.88% to 28.77%, with the highest average performance increase across three different class balancing method reaching 18.433%.
Initial study of general theory of complex systems: physical basis and philosophical understanding Esenovich Suleimenov, Ibragim; Arshavirovich Gabrielyan, Oleg; Serikuly Bakirov, Akhat
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7976

Abstract

The fundamental difference between neural networks containing and not containing feedback between elements is analyzed. It is shown that in the first of these cases, quantitative relationships describing the functioning of the neural network can be obtained based on an analogy with the theory of noise-resistant codes. In the second case, an analogy with electronic circuits that form memory cells (triggers) is valid. It is shown that feedback between elements of even the simplest neural networks can lead to the appearance of multidimensional hysteresis, when, with the same state of inputs, the system can be in several qualitatively different states, the transition between which can be abrupt. In this case, the state of the neural network outputs depends not only on the current state of its inputs, but also on the path along which this state was formed. The results obtained are used for the philosophical substantiation of a new approach to the interpretation of complex systems of various natures, which are considered analogs of neural networks. According to it, a system that can store and processing information should be considered "complex".
Electrical active power optimization of the SEIG-WTS based on perturb and observe method Borja Borja, Mario G.; Lescano, Sergio; Torres Alvarado, Sally; Yancachajlla Tito, Ubaldo; Luyo, Jaime
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.6940

Abstract

This paper proposes an electrical active power optimization for self-excited induction generator-wind turbine system (SEIG-WTS) using a perturb and observe (PO)-based maximum power point tracking. The main advantage is the optimization of the SEIG active power of SEIG-WTS and the simply practical implementation with rotor speed sensor, current sensor and three phase inverter. The active power optimization of SEIG-WTS is achieved by perturbing angular magnetic field speed with stator reference voltage. To test the effectiveness of the proposal, numeric simulations were carried out under very challenging conditions with wind speed profile in steps and actual wind speed profile. The proposal reaches the maximum power in 7 seconds for hardest condition when the system works at high rotor speed. The proposal is useful for the development of maximum power point tracking control (MPPT) controllers due to its simplicity in implementation.This paper proposes an electrical active power optimization for self-excited induction generator-wind turbine system (SEIG-WTS) using a perturb and observe (PO)-based maximum power point tracking. The main advantage is the optimization of the SEIG active power of SEIG-WTS and the simply practical implementation with rotor speed sensor, current sensor and three phase inverter. The active power optimization of SEIG-WTS is achieved by perturbing angular magnetic field speed with stator reference voltage. To test the effectiveness of the proposal, numeric simulations were carried out under very challenging conditions with wind speed profile in steps and actual wind speed profile. The proposal reaches the maximum power in 7 seconds for hardest condition when the system works at high rotor speed. The proposal is useful for the development of maximum power point tracking control (MPPT) controllers due to its simplicity in implementation.
Improving the performance of a U-shaped patch antenna using metamaterials for biomedical applications Siraj, Younes; Foshi, Jaouad; Saidi Alaoui, Kaoutar
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8283

Abstract

This paper discusses the performance improvement of a patch antenna using metamaterials (MTM). The suggested antenna is a U-shaped patch antenna with a modified ground plane dedicated to biomedical applications. The size of the antenna is 40×20 mm2 with a FR4 substrate (εr=4.3, tanδ=0.02, H=1.6 mm) designed for operation at 2.4 GHz (ISM Band) and 6.23 GHz frequencies. The proposed MTM is 2×2 array positioned under the antenna at a distance of 2 mm. The integration of the MTM enhances clearly the antenna performance especially the return loss, voltage standing wave ratio (VSWR) and the gain. However, the reflection coefficient was enhanced from -10.71 dB to -36.63 dB at 2.45 GHz and from -13.88 dB to -36.54 dB at 6.23 GHz, the VSWR improved from 1.66 to 1.03 at 2.45 GHz and from 1.75 to 1.04 at 6.23 GHz. Additionally, the peak gain also was increased from 1.77 dB to 3.48 dB. The obtained results confirm the suitability of the suggested antenna for biomedical applications.
Fusing Xception and ResNet50 features for robust grape leaf disease classification Vo, Hoang-Tu; Chau Mui, Kheo; Nguyen Thien, Nhon
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7269

Abstract

Grapes are one of the most widely cultivated fruits worldwide, and their economic and nutritional value makes them a significant crop in agriculture. However, grape plants are vulnerable to various diseases that can have detrimental effects on crop yield and quality. Accurate and timely identification of grape leaf diseases is crucial for efficient disease control and ensuring sustainable viticulture practices. In this study, we present a disease classification model specifically designed for grape leaves. The model incorporates bilinear pooling, utilizing the intermediate features extracted from two powerful convolutional neural network (CNN) models, Xception, and ResNet50. The outer product operation is applied to the extracted features, enabling the capture of intricate interactions and relationships between the features. The accurate classification of grape leaf diseases provided by our model offers significant benefits for grape farmers, vineyard owners, and agricultural researchers. It facilitates early disease detection, enabling proactive disease management strategies. Additionally, it assists in optimizing crop health, minimizing yield losses, and ensuring sustainable grape production.

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