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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 3,126 Documents
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Non-prioritized channel assignment improvement based on call traffic intensity and artificial neural network Adeyinka Ajao Adewale; Oritsematosan Laura Whyte; Omolola Faith Ademola; Gabriel Oluwatobi Sobola
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.8596

Abstract

The non-prioritized (NP) channel assignment model is characterized by a high call dropping probability (CDP) of handover calls and an increasing mobile call traffic volume due to the proliferation of mobile devices. In this study, the one-dimensional Markovian NP model has been improved upon using an artificial neural network (ANN) as a prediction mechanism of CDP using predicted traffic intensity and channel parameters to assign calls of different types to channels. A simulation comparison of the CDP of existing NP channel assignment with the NP with traffic intensity (CDPT) and with the ANN traffic intensity prediction model (CDPANN) was carried out and the study shows that the CDP was reduced drastically when the NP channel assignment with ANN assisted trained model was used putting signal quality into consideration. The CDPT has reduced CDP by 3%, 15%, and 40%, while the CDPANN has reduced CDP by 6%, 20%, and 50% for signal quality factors of 0.2 (poor), 0.5 (good), and 0.8 (very good) respectively. This study has shown that under varying radio frequency signal quality conditions, the ANN assisted channel assignment approach will help minimise the problem of high CDP associated with NP channel assignment and thereby improve ubiquitous mobile communication.
Deep spatiotemporal signal learning with transformers for multi-day wildfire forecasting Parul Dubey; Gaurav Vishnu Londhe; Vinay Keswani; Akshita Chanchlani; Murtuza Murtuza; Pushkar Dubey
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10936

Abstract

Wildfire forecasting is a critical challenge in environmental signal processing and disaster response planning. The ability to interpret multimodal spatiotemporal signals is essential for early warning systems and resource deployment. This study addresses these limitations by proposing a unified prediction-to-action framework. We utilized four open-access datasets—wildland fire emissions database (WFED), fire information for resource management system (FIRMS), Sentinel Hub, and a custom moderate resolution imaging spectroradiometer+shuttle radar topography mission (ERA5+MODIS+SRTM) fusion—covering fire occurrences, vegetation indices, meteorological parameters, and topographic features. These heterogeneous signals were preprocessed, aligned, and transformed into structured tensors for model training and evaluation. We use a transformer-based system to understand long-term patterns in space and time, enhanced by a belief–desire–intention (BDI) reasoning module that connects our predictions to flexible wildfire response plans. The novelty lies in the integration of signal-aware attention mechanisms with symbolic decision modeling. Model performance was evaluated using F1-score, intersection over union (IoU), mean absolute error (MAE), and directional accuracy. The suggested framework did better than the basic convolutional neural network (CNN) models, reaching an F1-score of 0.74, a directional accuracy of 84.3%, and lowering the MAE to 7.6 km², while also providing clear and relevant action suggestions.
Evaluating solar photovoltaic panel orientations for an open-field internet of things framework Khairul Anuar Mohamad; Mohamad Syahmi Nordin; Hairul Hafizi Hasnan; Ahmad Fateh Mohamad Nor; Rohaiza Hamdan; Nazirah Mohamad Abdullah; Nor Anija Jalaludin
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10597

Abstract

This paper presents a comparative experimental analysis of horizontal, vertical, and 45° tilt photovoltaic (PV) panel orientations, evaluated with and without load conditions in a tropical environment. Simultaneous measurements of open-circuit voltage (Voc), short-circuit current (Isc), and average power were conducted over three consecutive days to facilitate orientation-specific performance comparisons. Results show that the horizontal orientation demonstrated robust midday performance, achieving 3.65 W with load on one of the test day, but declined sharply in post-noon periods. The vertical orientation consistently produced lower average power outputs, approximately 3.1 W with load. As a reference, the 45° tilt consistently produced the highest output, with average load powers of 3.77 W, 3.41 W, and 3.64 W over the three days. This performance exceeded horizontal orientations by 2–5% and vertical orientations by 15–20%. Both horizontal and tilt orientations consistently surpassed internet of things (IoT) operational thresholds of 3.3–5.0 V and 100–200 mA required for low power sensor nodes, ensuring excess energy for storage. In contrast, the vertical orientation posed a risk of inadequate current in late afternoon periods. Thus, the results indicate that the orientation selection should be environment-driven. Horizontal or tilted orientations are suitable for rural and open-field IoT settings, while vertical orientations are advantageous for space-constrained or dust-prone environments.
GNN data association based multi-target localization and vital signs monitoring using SIMO-UWB sensor network Meraouli Hadjer Rania; Slimane Zohra; Abdelmalek Abdelhafid
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10530

Abstract

This paper presents a method for multi-target localization and vital signs monitoring using impulse ultra-wideband (UWB) technology. A single-input multiple-output (SIMO) sensor network is employed to enable simultaneous signal reception, which introduces challenges in signal separation and precise target positioning. The primary difficulty arises from the data association problem, where measurements must be correctly assigned to their respective targets, particularly in cases of overlapping signals or closely spaced targets. To address this issue, a time of arrival (TOA) algorithm is applied to estimate target ranges, while all possible measurement–target associations are evaluated. Subsequently, true target positions are obtained through trilateration combined with the global nearest neighbor (GNN) method. For vital signs monitoring, the continuous-time Fourier transform(CTFT) is utilized to estimate respiratory and cardiac rates. Experimental results demonstrate high accuracy, with relative errors of 1.42% in distance estimation, 2.34% in breathing rate, and 0.73% in heart rate estimation.
Three-phase voltage dips in load nodes of the electrical network of the Santiago de Cuba province measure for its correction José Ricardo Nuñez-Alvarez; Janette Cervantes-Oliva; Ramon Antonio Zambrano-Mero; Hernan Hernández-Herrer; Yolanda Llosas-Albuerne
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10172

Abstract

The growing need to supply quality electrical energy to end-users has given rise to various studies and research on the technical parameters that this energy should possess. In addition, many users perceive that many of the problems in the operation of their household appliances are related to the quality of the electrical energy that reaches their homes. One of the fundamental problems affecting the quality of electrical power is the so called voltage dips, which are nothing more than disturbances of great relevance due to their direct consequences, secondary effects, and frequency of occurrence. In this research, a study is conducted to evaluate the occurrence of three-phase voltage dips in a simplified electrical network in Santiago de Cuba, Cuba. As a result, the Santiago Norte substation presents the most detrimental behavior. In addition, 50% of the simulations performed on the behavior of the Santiago East Substation, which handles a voltage of 13.8 kV, showed that it is the substation where the deepest voltage dips occur. Based on the results obtained, it has been decided to change the transformer tapping of the affected substations to reduce the occurrence of voltage dips and ensure stable operation in the event of three phase faults in the power system under analysis.
Instant-incidental-close-loop scheme to improve battery life in wireless sensor applications Yopi Sopian; Faizal Arya Samman; Muhammad Niswar
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.8655

Abstract

This work presents a simple new idea for implementing algorithms in microcontrollers that govern duty cycle transition settings in wireless sensors in closed-loop circuits that run on batteries. The switching time controlled to get end point and average duty-cycle (STEADY) method is used. Duty cycle regulation algorithms are used by instantaneous incidental closed loop (IICL) schemes to regulate current flow in circuits that serve as power consuming loads, facilitating quick and seamless transitions from open to closed loop. By using the STEADY algorithm on IICL, the issue of conserving battery energy to increase battery life can be resolved. Additional advantages of the STEADY algorithm include its low memory utilization of 4 Kbytes, or 12.5% of the 32 Kbytes of available memory slot, simplicity, and ease of implementation on microcontroller devices. Final testing using the IICL STEADY algorithm on the VD-2023 wireless DC voltage sensor prototype series shows that 627 mAh is more energy efficient than battery life in the IICL scheme without the STEADY algorithm applied, which is 26 minutes 37 seconds. The wake time is set to one millisecond, and the sleep time is set to one minute, and the battery power is provided by a 280 mAh battery. This results in a battery life of 8 hours 16 minutes 57 seconds.
Video classification of Indonesian traditional dance using a hybrid CNN-LSTM model with pose estimation Candra Irawan; Heru Praomono Hadi; Cahaya Jatmoko; Mohamed Doheir
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11093

Abstract

The preservation and recognition of traditional Indonesian dances face challenges due to limited digital documentation and declining intergenerational transmission. Manual annotation of dance videos is time-consuming and prone to subjectivity, creating urgency for automated solutions. This study proposes a deep learning-based approach combining convolutional neural networks (CNN) for spatial feature extraction and long short-term memory (LSTM) for temporal modeling to recognize traditional dance movements from video sequences. The system leverages OpenPose for keypoint detection and gesture estimation, enabling frame-wise pose representation prior to classification. A hyperparameter tuning process was applied to optimize the CNN-LSTM architecture using 80% of the dataset for training and 20% for testing. Experimental results show the proposed model achieved a macro accuracy of 98.4%, with perfect precision, recall, and F1-score. This research contributes to cultural heritage digitization and intelligent video analysis by enabling accurate, real-time classification of traditional dances, providing a foundation for future systems in education, archiving, and motion-driven applications.
Design and performance analysis of slot-based DGS-MIMO for sub-band allocation in 5G/6G networks Priethamje Vithya K; Varalakshmi L M
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10389

Abstract

Wireless communication systems are quickly evolving to fulfil the increasing need for higher spectral efficiency, lower latency, and seamless connectivity. Although the conventional multiple-input multiple-output (MIMO) architecture improves reliability and throughput, it still faces high hardware complexity, spectral inefficiency, and interference issues in dense environments. Existing solutions, such as double-sided microstrip patch antennas or 8×8 MIMO arrays, yield moderate improvements but are not flexible enough for next-generation networks. To overcome these limitations, this paper puts forward a slot-based distributed generalized spatial modulation (DGS-MIMO) framework, which integrates dynamic antenna subset activation and adaptive sub-band allocation. In this way, the number of radio frequency (RF) chains is reduced, power consumption is lower, and spectral utilization is improved. Experimental validation shows excellent impedance matching (return loss up to -36.29 dB), high gain (6.98 dB), high radiation efficiency, and low signal reflection (voltage standing wave ratio (VSWR) as low as approximately 1.03). These results prove the robustness and efficiency of the proposed system in comparison with conventional designs. Besides the performance enhancement, the framework has great potential to be applied in real-world 5G/6G applications, especially in internet of things (IoT) deployments and vehicular communication scenarios where scalability, energy efficiency, and reliability are important.
A mathematical model to cluster reviewers for online review system Runa Ganguli; Akash Mehta; Takaaki Goto; Soumya Sen
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9620

Abstract

Online business models accept reviews or feedback from customers which are processed and analyzed for important business decisions. Online reviews are helpful to understand the usefulness or popularity of a product. However, it has been observed that sometimes fake reviews are frequently used to boost the popularity of one's own product or to damage reputation of competitors' products. Henceforth it is an interesting research problem to validate reviews or trustworthiness of reviewers. In this paper, a mathematical model is introduced to rate and cluster reviewers based on relevant parameters. It has been observed from business intelligence perspective, that grouping reviewers into different clusters, rather than ranking them individually based on their authenticity, would be more beneficial for potential buyers to understand the quality of reviewers. In the proposed model, clustering is performed using two weighted scores based on average opinion variance and product price. The mean shift clustering algorithm is used to dynamically slab the product price attribute while Jenks Natural Breaks Optimization (JNBO) method and K-means algorithm are applied for the reviewer clustering. Further this research work analyses the impact of product price on reviewer rating and validates the result using t-test statistical method. The proposed methodology is experimented on Amazon datasets to show efficacy of the model.
Design and analysis of hybrid filters for harmonic reduction in three-phase nonlinear rectifier loads Yulianta Siregar; Daniel Julian Sinaga; Nur Nabila Mohamed; Dedet Candra Riawan; Muldi Yuhendri
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10971

Abstract

Rectifiers are included in the non-linear load category and can create resonance in a distribution network, which can be called harmonics. In a distribution network, rectifiers (non-linear loads) distort voltage and current harmonics, resulting in power losses and reduced power quality in the network. Harmonics in the distribution power grid cannot be eliminated, but can be reduced to values that can still be tolerated and evaluated until they are by the IEEE 519-2014 Standard. This research discusses reducing harmonic values in distribution networks designed in a simulation to implement a combination (hybrid) of series active and passive filters of the high pass and high pass type-C types. The results of research using the installation of high pass filters, type C filters, series active filters, hybrid-1 filters, and hybrid-2 filters, respectively, for each current total harmonic distortion (THD) value are 15.93%, 16.15%, 8.51%, 0.27%, and 0.25% where the current THD value when using the two hybrid filters is below the specified standard limit, namely 5%. By installing a hybrid filter, the harmonic value in each feeder is much better than installing a single series active filter, a high pass filter, and a type-C filter.

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