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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 14, No 5: October 2025" : 73 Documents clear
Design and implementation of a solar-powered IoT-based real-time air quality monitoring system Soemphol, Chaiyong; Thongsan, Taweesak; Ninkaew, Sakuntala; Panmuang, Piyapat
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10473

Abstract

Air pollution has become a global issue due to rapid urbanization and industrialization. Air quality monitoring is essential for mitigating the adverse effects of air pollution on public health and the environment. This study presents a solar-powered internet of thing (IoT)-based air quality monitoring system designed for autonomous operation in outdoor settings. The prototype integrates an ESP32 microcontroller with low-cost sensors for PM2.5, PM10, temperature, humidity, and heat index. Powered by a solar panel and battery, the system ensures off-grid functionality, while Wi-Fi transmission to the Blynk platform, enables real-time visualization, historical record storage, and instant user access through mobile dashboards. The system was calibrated against reference instruments and deployed for 14 consecutive days. Results confirmed stable data transmission and reliable performance that suitability for outdoor use without reliance on grid power under real-world conditions. Furthermore, correlation analysis showed a strong relationship between PM2.5 and PM10, and moderate associations with humidity. Regression analysis further identified humidity and heat index as the most significant predictors, while temperature exhibited only minor influence. These findings demonstrate the feasibility of a low-cost, portable, and energy-autonomous IoT monitoring system, providing accurate real-time insights to support evidence-based air quality management.
Artificial intelligence based on fuzzy logic for a long-range radio frequency identification reader antenna Sefraoui, Hanane; Derkaoui, Abdechafik; Ziyyat, Abdelhak
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9309

Abstract

The radiation pattern of radio frequency identification (RFID) antennas is influenced by various factors such as design, operating frequency, and polarization, which determine characteristics like directionality, omnidirectionality, beam width, and gain. Achieving precise readings over extended distances is crucial for the effectiveness of RFID systems, enabling faster item retrieval and delivery. The read distance, a critical aspect of RFID system performance, depends on factors like transmitted power, frequency, and antenna gain. Passive backscatter RFID setups particularly benefit from optimizing read distance for efficient operation. Fuzzy logic, as a soft computing technique, addresses uncertainties inherent in RFID systems effectively. This paper presents a novel approach to RFID antenna design, utilizing fuzzy logic to dynamically adjust frequency and power transmission. By enhancing field distribution, polarization, and received signal strength, this approach aims to optimize tag readings at extended distances, thereby improving overall system effectiveness. The methodology involves implementing algorithms in a C program to control the long-range distance aspects of the RFID system. Incorporating fuzzy rule algorithms into the RFID system's control logic enhances its ability to respond intelligently to changes in the operating environment, contributing to improved performance and reliability in long-range RFID applications.
Autoregressive integrated moving average-long short-term memory optimized hybrid model for cybercrime forecasting Martin Morales-Barrenechea, Manuel; Rodriguez, Ciro; David Cancho-Rodriguez, Ernesto; Richard Huamantingo Navarro, Ricardo
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9769

Abstract

Cybercrime represents a growing global threat with adverse impacts on citizen security, the digital economy, and quality of life. In this context, an optimized hybrid model was developed that combines autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) for the monthly forecast of cybercrime complaints, applying the cross industry standard process for data mining (CRISP-DM) methodology and applying Python based data science techniques. The model combines the capabilities of the ARIMA statistical approach to capture linear components with the power of LSTM neural networks to address nonlinear temporal relationships. The architecture was trained on a set of 60,378 official records of complaints registered by the National Police of Peru between 2018 and 2023, achieving a mean absolute percentage error (MAPE) of 10.73%, which represents a significant improvement over the singular ARIMA and LSTM predictive models. Compared to previous studies in crime, health, and agriculture, this approach showed a greater ability to generalize over complex time series. It is concluded that the application of the proposed model is a relevant contribution for the police and other security agencies to anticipate crime trends and design preventive and effective strategies to combating cybercrime.
Fixed and fair power allocation in downlink and uplink NOMA: outage probability analysis and bit error rate comparative study Falloun, Abdelbari; Ait Madi, Abdessalam
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9630

Abstract

Non-orthogonal multiple access (NOMA) is a crucial technology for upcoming radio access networks since it allows several users to use the same time and frequency resources. It is positioned as a viable option for next-generation communication systems because to its capabilities to increase system capacity and spectrum efficiency. This essay investigates the effects of fair and fixed power allocation (PA) techniques on NOMA systems' uplink and downlink performance. It specifically assesses bit error rate (BER) and outage probability (OP), two crucial performance parameters. The paper provides a thorough comparison of the fixed and fair PA approaches, highlighting the advantages, and disadvantages of each. While fixed PA is easier to deploy, results show that it performs poorly in dynamic situations, increasing BER and OP, particularly for users with less reliable channels. Fair PA, on the other hand, improves system dependability, and user fairness by dynamically allocating power depending on user situations, thus reducing OP and BER. Future wireless networks will benefit greatly from its enhanced spectrum efficiency and up to 78% reduction in outage likelihood. With fair PA's higher flexibility and effectiveness in real-world, varied circumstances, the results underline the significance of selecting appropriate PA techniques for NOMA systems.
Sinusoidal modelling for efficient source coding of phonocardiogram signals in cardiac monitoring devices Alabed, Samer; Al-Rabayah, Mohammad; Al-Sheikh, Bahaa; Farah, Lama Bou
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9806

Abstract

This study focuses on developing an efficient and cost-effective approach for compressing Phonocardiogram (PCG) signals without compromising their quality. The method utilizes two data compression techniques, capturing heart sounds and transforming them into the frequency domain to extract essential features such as frequency, phase, and amplitude peaks. The compressed signals are subsequently reconstructed to faithfully replicate the original heart sounds. The findings contribute to advancements in biomedical signal processing and compression methodologies, with potential applications in telemedicine and remote sustainable healthcare systems. Compressed PCG signals enable real-time remote consultations and continuous cardiac health monitoring, particularly in underserved regions with limited medical resources. This research holds significant potential for improving access to cardiovascular healthcare and promoting overall health and well-being.
Performance analysis of a proximity-coupled triangular slot microstrip patch antenna for ship radar applications Ikhlef, Ismahene; Chemachema, Karima; Grine, Farouk
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9637

Abstract

Microstrip patch antennas are extensively utilized in modern communication systems because of their small size and simple fabrication process. Among the different patch geometries, triangular patches offer size reduction compared to their rectangular and circular counterparts, making them suitable for space-constrained applications. This study focuses on the design and analysis of an equilateral triangular microstrip antenna (ETMSA) using proximity coupled feed with a triangular slot, targeting optimal performance at 2.2 GHz. The antenna is constructed using two FR4 substrates of identical permittivity but different thicknesses (h1 and h2), with a 50-ohm microstrip line feed positioned between them. The aim is to determine the optimal values of patch surface area, slot dimensions, and upper substrate thickness to achieve maximum bandwidth, minimal return loss, and ideal voltage standing wave ratio (VSWR). Simulations and measurements confirm that the antenna achieves a 120 MHz bandwidth achieving a return loss of –42 dB and a VSWR of 1.03, demonstrating excellent agreement. These results confirm the antenna's effectiveness for fixed-beam applications in wireless communication systems, highlighting its potential for efficient and compact antenna solutions.
A deep Q-learning approach for adaptive cybersecurity threat detection in dynamic networks Bharathi, P. Shyamala; Selvaperumal, Sathish Kumar; Ramasenderan, Narendran; Thiruchelvam, V.; Annamalai, Deepak Arun; Reddy, M. Jaya Bharatha
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9494

Abstract

Cybersecurity faces persistent challenges due to the rapid growth and complexity of network-based threats. Conventional rule-based systems and classical machine learning approaches often lack the adaptability required to detect advanced and dynamic attacks in real time. This study introduces a deep Q-learning framework for autonomous threat detection and mitigation within a simulated network environment that reflects realistic traffic, malicious behaviors, and system conditions. The framework incorporates experience replay and target network stabilization to strengthen learning and policy optimization. Evaluation was performed on a synthesized dataset containing benign traffic and multiple attack categories, including distributed denial of service (DDoS), phishing, advanced persistent threats, and malware. The proposed system achieved 96.7% detection accuracy, an F1-score of 0.94, and reduced detection latency to 50 ms. These results surpassed the performance of rule-based methods and traditional classifiers such as support vector machines, random forests, convolutional neural networks, and recurrent neural networks. A central contribution lies in combining dynamic feature selection with reinforcement learning (RL), allowing the agent to adapt to evolving threats and diverse network conditions. The findings demonstrate the potential of deep reinforcement learning (DRL) as a scalable and efficient solution for real-time cybersecurity defense.
An internet of things-based weather system for short-term solar and wind power forecasting using double moving average Syafii, Syafii; Nur Izrillah, Imra; Aulia, Aulia; Ilhamdi Rusydi, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9852

Abstract

This article presents the design and implementation of an internet of things (IoT)-based weather forecasting system aimed at optimizing operational planning for renewable energy generation. The system leverages a Raspberry Pi as its central controller, integrating pyranometer and anemometer sensors for real-time data collection and predictive analytics. Utilizing the double moving average method, the system provides accurate short-term forecasts of solar and wind power outputs, which are crucial for addressing the intermittency challenges of renewable energy sources. The integration with the Blynk platform ensures user-friendly data visualization and accessibility. Results from a three-day testing phase reveal the system's high accuracy, with prediction errors of 8.79% for solar power and 16.49% for wind power. These findings underscore the system's potential to enhance energy planning, improve efficiency, and support sustainability goals. By enabling data-driven decision-making, this IoT-based forecasting system offers a scalable solution for advancing renewable energy integration into the power grid.
Text clustering for analyzing scientific article using pre-trained language model and k-means algorithm Firdaus, Firdaus; Nurmaini, Siti; Yusliani, Novi; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Kunang, Yesi Novaria; Fachrurrozi, Muhammad; Armansyah, Risky
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9670

Abstract

Text clustering is a technique in data mining that can be used for analyzing scientific articles. In Indonesia-accredited journals, SINTA, there are two languages used, Indonesian and English. This is the first research focusing on clustering Indonesian and English texts into one cluster. In this research, bidirectional encoder representations from transformers (BERT) and IndoBERT are used to represent text data into fixed feature vectors. BERT and IndoBERT are pre-trained language models (PLMs) that can produce vector representations that take care of the position and context in a sentence. To cluster the articles, the K-Means algorithm is implemented. This algorithm has good convergence and adapts to the new examples, which helps in improved clustering performance. The best k-value in the K-Means algorithm is defined by using the silhouette score, the elbow method, and the Davies-Bouldin index (DBI). The experiment shows that the silhouette score can produce the most optimal k-value in clustering the articles, which has a mean score of 0.597. The mean score for the elbow method is 0.425, and for the DBI is 0.412. Therefore, the silhouette score optimizes the performance of PLMs and the K-Means algorithm in analyzing scientific articles to determine whether in scope or out of scope.
Improving COVID-19 chest X-ray classification via attention-based learning and fuzzy-augmented data diversity Cheluvaraju, Girish Shyadanahalli; Shivasubramanya, Jayasri Basavapatna
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.10861

Abstract

This paper presents a hybrid deep learning (DL) framework that combines model-level and data-level enhancements to improve classification performance without compromising clinical relevance. The proposed framework consisted of an EfficientNetB0 model with a hybrid attention module, which focused attention both spatially and channel-wise, and a VGG-16 model that was trained on training data augmented using a fuzzy-logic-based contrast and brightness enhancement. The attention module focused the model by recalibrating the features in an adaptive manner. The fuzzy-logic augmentation increased data diversity while maintaining the anatomical fidelity of the medical image domain. In addition, an uncertainty-aware ensemble approach was utilized to combine both models' predictions, which considered model confidence and entropy of the predictions, to enhance the reliability of the predictions. The proposed framework achieves a classification accuracy of 99.6%, outperforming several existing approaches.

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