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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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Enhanced security and performance through permutation-byte key cipher with reduced-round AES Baladhay, Jerico S.; Danganan, Alvincent E.; Reyes, Edjie M. De Los; Gamido, Heidilyn V.; Gamido, Marlon V.
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.8750

Abstract

This paper introduces the permutation-byte key cipher with reduced-round advanced encryption standard (PBKC-RRAES), a novel enhancement of the AES designed to significantly improve both security and performance. The proposed algorithm integrates key modifications; i) replacing the computationally intensive MixColumns function with an efficient bit permutation technique that achieves superior diffusion while reducing computational overhad by eliminating complex matrix multiplication operations. This substitution enhances security through improved bit-level scrambling patterns, while simultaneously accelerating processing speed through simpler bitwise operations; ii) the addition of AddRoundKey operations between cipher states, iii) enhanced byte substitution operations and round constant additions in the key schedule algorithm before key expansion, and iv) reducing rounds from 10 to 6. These innovations yield heightened sensitivity to plaintext changes, evidenced by a 54.214% avalanche effect, surpassing the standard 50% threshold. Performance evaluations reveal PBKC-RRAES operates 26.90% improvement in encryption time and a 22.73% improvement in decryption time than standard AES, alongside throughput enhancements of 39.48% in encryption and 31.27% in decryption compared to the original AES, critical improvements for bandwidth-constrained applications. These results demonstrate that PBKC-RRAES is a robust and effective alternative for cryptographic applications, particularly beneficial for real-time video streaming, secure cloud storage, mobile payment systems, and IoT device where both security and processing effectivity are paramount.
Analysis of voltage drop using transformer tap changer and placement of capacitor bank with genetic algorithm Siregar, Yulianta; Kivander Saragi, Agus; Ngamroo, Issarachai
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10722

Abstract

The demand for electrical energy is increasing due to high economic growth and population. The impact is that electrical energy operates excessively to meet the required demand. Unbalanced loads, higher power losses on the line, and voltage drops that are higher than allowed are just a few of the issues that may result from this. Adding tap changers and capacitor banks is one method of improving the voltage profile and power losses. To conduct this study, tap changers and capacitor banks were added to the IEEE 33 bus network system. The value, capacity, and location of the tap changers and capacitor banks in the system were ascertained using the genetic algorithm (GA) approach. According to the simulation results, the voltage profile, which initially had 21 buses outside the IEEE standard limits, may be ideal by installing two tap changers and two capacitor banks. Additionally, reactive power losses decreased from 41.8 kVar to 93.3 kVar, and active power losses decreased from 202.7 kW to 130.7 kW, a decrease of 72 kW.
5G cellular network planning in Parepare City Yuniarti, Yuniarti; Dase, Sulwan; Khaerunnisa, Nurul; Litha, Arni; Nurhayati, Nurhayati; Dzar Faraby, Muhira; Amaliah, Asma; Isminarti, Isminarti; Pineng, Martina; Palinggi, Sandryones
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10716

Abstract

The telecommunications industry is rapidly advancing, particularly in cellular network communications that use air as the transmission medium, with 5G new radio (NR) emerging as a key global technology including in Indonesia. Defined by enhanced mobile broadband (eMBB) offering speeds up to 10 Gbps, ultra-reliable low-latency communications (URLLC) with latency below 1 millisecond, and massive machine-type communications (mMTC) supporting large-scale internet of things (IoT) connectivity, 5G plays a crucial role in modern digital infrastructure. This study focuses on the city of Parepare in South Sulawesi, an area driven by trade, port operations, fisheries, shipbuilding, and natural tourism highlighting the need for high-speed and reliable data services. The research aims to develop a comprehensive 5G NR network plan for Parepare through coverage and capacity analyses evaluating synchronization signal-reference signal received power (SS-RSRP), signal-to-interference-plus-noise ratio (SS-SINR), and throughput performance. Using Atoll software to design and map next-generation Node B (gNodeB) placements, the study offers a scientific approach to optimizing 5G deployment and supporting the city’s economic growth and tourism potential.
Generating data for predicting court decisions in Kazakhstan using machine learning Ignatovich, Artyom; Yessengeldina, Anar; Baidullayeva, Gulzhakhan; Ussipbekova, Dinara; Jakhanova, Baktykul; Saduakassova, Gulmira; Serimbetov, Bulat; Tynykulova, Assemgul
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10490

Abstract

This study presents the development of a synthetic dataset and machine learning models for predicting court decisions in Kazakhstan. The dataset contains 100,000 cases generated from the Code of the Republic of Kazakhstan, covering both administrative and criminal offenses. Each record includes attributes such as the age of the accused, offense type and severity, and mitigating or aggravating factors. Regression models were applied to estimate offense severity, level of guilt, and likelihood of penalties, while classification models predicted the offense category, relevant law articles, and sentencing type. Predictions addressed both general outcomes—classifying cases as criminal or administrative—and specific judicial decisions, including fines, imprisonment terms, and other penalties. Classification models achieved 92% accuracy in determining offense category and sentencing type, and regression models reached a root mean squared error (RMSE) of 0.12 for offense severity. Using synthetic data preserves confidentiality while enabling pattern discovery for decision support. The results demonstrate the potential of artificial intelligence (AI) to improve sentencing prediction, prioritize case processing, and enhance transparency in Kazakhstan’s judicial system. Beyond transparency in decision support, the proposed approach also shows potential in crime prevention, workload optimization, and fostering digital transformation within judicial operations.
System dynamics modeling for strategic management of information technologies in universities Andrade-Arenas, Laberiano; Giraldo Retuerto, Margarita; Yactayo-Arias, Cesar
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10618

Abstract

This study seeks to answer the question: how can system dynamics (SD) modeling contribute to the strategic management of information technology (IT) in universities? The objective of the research is to analyze the importance of incorporating IT into university strategic management through the application of SD methodology. To this end, a model was designed that integrates variables related to resource allocation, the quality of the educational process, and the interaction between institutional actors. The methodology made it possible to simulate technological implementation scenarios and examine their effects on operational efficiency and academic performance. The results show that the strategic integration of IT promotes better resource planning, optimizes the interaction between administrative and academic processes, and contributes to raising the quality of teaching. In conclusion, the proposed model demonstrates that SD is an effective tool for anticipating and understanding the internal dynamics of universities, facilitating more efficient strategic management in today's digital context.
A hybrid random forest and particle swarm optimization model for early preeclampsia detection Yuandari, Esti; Rahman, R. Topan Aditya; Haryono, Ika Avrilina; Hidayah, Nurul; Iswandari, Novita Dewi; Hateriah, Siti
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10721

Abstract

Preeclampsia has become a serious medical problem in the world. Currently, there is no routine or comprehensive screening program in place for preeclampsia, which means that preventive measures are not as effective as they could be, potentially resulting in higher rates of illness and death among mothers and infant. The main purpose of this study is to predict early of preeclampsia using random forest algorithms. This study used a quantitative approach with samples 504. The data were analyzed using random forest with particle swarm optimization (PSO). Random forest have been an accuracy rate of 96.08%, for the area under the curve (AUC), precision, sensitivity, and specificity each (0.971; 97.06%; 97.06%; and 94.12%). Model significantly increased 1.39% after optimize from 94.69% to 96.08%. The design process model algorithm has been validated that have a high level of accuracy based on literature reviews. The quality of services offered will certainly influence people to utilize technology-based services more than conventional ones. Recommendation for field technology and health is building an application model for early prediction of preeclampsia based on machine learning (ML) which is an effort for health workers to provide optimal antenatal care and step in changing technology-based pregnancy checks as initial prevention for pregnant women so that preeclampsia can be avoided.
Design and analysis of an asymmetrical star-shaped fractal antenna with meta-surface integration at 5.2 GHz Dalsania, Piyush; Rathod, Jagdish M.
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10163

Abstract

Wireless communication requires optimized antenna designs to ensure maxi mum signal reception and transmission in today’s rapidly advancing technolo gies. Recent research emphasizes improving antenna efficiency and directivity to support higher data rates, extended coverage, and reliable connectivity. How ever, conventional antenna structures often suffer from narrow bandwidth, low radiation efficiency, and high return loss, which degrade signal quality and re strict operational range, particularly in complex electromagnetic environments. This study introduces an innovative asymmetrical star-shaped fractal antenna coupled with a metasurface layer consisting of periodic split-ring resonator (SRR) unit cells on a FR4 substrate to overcome these restrictions. The SRR based metasurface plays a critical role in suppressing surface waves, improving impedance matching, and enhancing radiation directivity. Experimental evalu ations were performed across 4.5–10 GHz, focusing on key performance mea sures such as gain, return loss, and voltage standing wave ratio (VSWR). The suggeted antenna achieved a stable return loss below −10 dB and demonstrated a strong operational peak at 5.2 GHz, with improved directivity and radiation efficiency compared to conventional patch designs. The integration of asym metrical star-shaped fractal geometry with SRR-based metasurface technology effectively addresses the shortcomings of traditional antennas, establishing the proposed design as a compact, efficient, and reliable candidate for mid-band wireless communication systems.
Meta-learning for malaria diagnosis: evaluating stacking models for enhanced classification performance Napa, Komal Kumar; Murugan, Sangeetha; Subramanian, Sathya; Saravanan, Durga Devi; Nageswari, Devana; Prasad, Battula Krishna
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10158

Abstract

Accurate malaria detection is crucial for effective disease management, particularly in regions with limited medical resources. Deep learning models have shown promising results in automated diagnosis, yet real-world deployment often faces challenges such as computational cost and model interpretability. This study evaluates multiple deep learning architectures—VGG16, ResNet50, InceptionV3, MobileNetV2, and DenseNet121—on the publicly available National Institutes of Health (NIH) malaria cell image dataset (27,558 images), and enhances their performance using stacking ensemble learning with different meta-learners. Among individual models, DenseNet121 achieved the highest accuracy of 88.00%, while MobileNetV2 had the lowest at 84.80%. Implementing stacking with logistic regression as the meta-learner improved accuracy to 89.40%, while random forest increased it to 90.10%. The best performance was achieved with XGBoost as the meta-learner, attaining an accuracy of 91.20%, precision of 92.10%, recall of 90.80%, and an F1-score of 91.40%—representing a 3.2% accuracy improvement over the best individual model. The classification report further confirms superior performance in distinguishing infected and uninfected cases. These results highlight the potential of stacking with advanced meta-learners to support health workers in rapid, reliable malaria diagnosis, ultimately aiding timely treatment, and improving patient outcomes in clinical and field settings.
Thermal analysis of li-ion battery pack using phase change materials based on climate conditions Mohammed Mokhtar Benounnane, Ishak; Wahid Belarbi, Ahmed; El Bachir Ghribi, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9641

Abstract

The development of lithium-ion batteries necessitates improved management of these systems, particularly with regard to thermal aspects. They operate optimally between 35 °C and 45 °C. Temperatures exceeding 50 °C accelerate cell aging, while those surpassing 60 °C can trigger thermal runaway, potentially leading to catastrophic failure. To mitigate these risks, phase change materials (PCMs) are employed in battery thermal management systems (BTMS). They absorb heat during charging or discharging, transitioning from solid to liquid, then release the stored energy during periods of low demand, solidifying to help regulate battery temperature. This study conducts a thermal analysis of a lithium-ion (LiFePO4) battery pack delivering a 24 V load, using COMSOL MULTIPHYSICS software. The objective is to evaluate and compare the thermal behavior of different PCMs, RT27, Paraffin Wax 58-60, and HM030, against air as a baseline reference. Simulations are performed using the integrated finite element method (FEM), with a discharge rate of 4 C. A correlation is proposed between the choice of PCM and the climate in specific locations, with the choice being made based on the disparities in the results obtained.
Design and emulation of an SDN network with opendaylight to improve QoS in a peruvian financial institution Roncal, Juan David Indigoyen; Paulino, Christian Ovalle
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.9895

Abstract

This study presents the design and emulation of a software-defined networking (SDN) architecture using the OpenDaylight controller to enhance the quality of service (QoS) in a Peruvian financial institution. The main objective is to overcome limitations of traditional networks, including high latency, limited bandwidth, and packet loss, which hinder the efficiency of financial services. The proposed SDN architecture was implemented and tested through simulations in the Eve-NG platform, where key performance parameters—latency, throughput, and packet loss—were measured. Results demonstrated significant improvements, with latency reduced by up to 40%, stable throughput maintained at 10 Mbps across all branches, and a noticeable reduction in packet loss. These outcomes validate the feasibility of adopting SDN in financial environments to support critical services and ensure operational continuity. Furthermore, the findings emphasize SDN’s role in modernizing network infrastructures, improving user experience, and aligning local financial institutions with international technological trends. Future research may explore alternative SDN controllers, scalability in larger topologies, and integration with emerging technologies such as network function virtualization (NFV).

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