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Buletin Ilmiah Sarjana Teknik Elektro
Published by Universitas Ahmad Dahlan
ISSN : 26857936     EISSN : 26859572     DOI : 10.12928
Core Subject : Engineering,
Electrical & Electronics Engineering
Buletin Ilmiah Sarjana Teknik Elektro (BISTE) adalah jurnal terbuka dan merupakan jurnal nasional yang dikelola oleh Program Studi Teknik Elektro, Fakultas Teknologi Industri, Universitas Ahmad Dahlan. BISTE merupakan Jurnal yang diperuntukkan untuk mahasiswa sarjana Teknik Elektro. Ruang lingkup yang diterima adalah bidang teknik elektro dengan konsentrasi Otomasi Industri meliputi Internet of Things (IoT), PLC, Scada, DCS, Sistem Kendali, Robotika, Kecerdasan Buatan, Pengolahan Sinyal, Pengolahan Citra, Mikrokontroller, Sistem Embedded, Sistem Tenaga Listrik, dan Power Elektronik. Jurnal ini bertujuan untuk menerbitkan penelitian mahasiswa dan berkontribusi dalam pengembangan ilmu pengetahuan dan teknologi.
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Articles 15 Documents
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Search results for , issue "Vol. 7 No. 2 (2025): June" : 15 Documents clear
DNA-based Cryptography for Internet of Things Security: Concepts, Methods, Applications, and Emerging Trends Ţălu, Mircea
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.12942

Abstract

DNA-based cryptography is an emerging field that combines molecular biology and computational security to develop novel encryption, secure data storage, and steganographic techniques. It offers a promising alternative to traditional cryptographic systems, addressing challenges like storage efficiency, robustness, and resistance to computational attacks. In the era of the Internet of Things (IoT), where massive networks of interconnected devices continuously generate and exchange sensitive data, ensuring secure communication and storage has become a critical challenge. DNA-based cryptography presents a unique opportunity to enhance IoT security by offering ultra-secure encryption methods that exploit DNA’s vast information density and inherent randomness. These encryption methods leverage the complexity of DNA encoding - such as nucleotide substitution, DNA strand pairing, and biological operations like splicing and amplification - to create security layers that are difficult to decipher using conventional computational techniques. Recent advancements in DNA synthesis, sequencing, and encoding methodologies have facilitated the development of encryption schemes tailored for IoT applications, enabling lightweight, high-capacity security solutions that outperform traditional cryptographic methods. Beyond IoT, DNA-based cryptography also holds potential in areas such as secure biomedical data storage, digital rights management, and archival of sensitive governmental or historical information, demonstrating its broader applicability across diverse domains. Future research should optimize DNA encoding, improve storage technologies, and harness artificial intelligence for real-time threat detection, automated encryption, and adaptive security in IoT systems. This review analyzes DNA-based cryptographic methods, including natural and pseudo-DNA encryption, DNA-based steganography, and hybrid models, while uniquely exploring their IoT applications, emerging trends, practical implementations, key advantages, challenges, and future research directions.
Improved Black Widow for Optimal Distributed Generation Placement in Radial Distribution Networks Egeruo, Sochima; Olatunde, Oladepo
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.12954

Abstract

Mitigating active and reactive power losses and improving voltage profiles in radial distribution networks remain critical challenges for system operators. While the introduction of Distributed Generation offers a promising solution, determining their optimal placement and sizing is a complex problem. Metaheuristic algorithms, though effective, have seen limited application in addressing issues specific to radial feeders, where traditional analytical methods dominate. This paper presents an Improved Black Widow Optimization Algorithm to improve Distributed Generation location and proper sizing in radial distribution networks. The Improved Black Widow Optimization Algorithm incorporates a non-linear inertia weight adjustment to enhance the balance between diverse exploration and focused exploitation, addressing a key limitation of the standard Black Widow Optimization. A backward-forward sweep algorithm is used to calculate the initial losses and voltage profile of the test systems, while the Improved Black Widow Optimization Algorithm determines optimal Distributed Generation parameters. The proposed method is tested on the IEEE 33-bus system and validated on a Nigerian 32-bus 11kV distribution feeder using MATLAB. Results demonstrate that the Improved Black Widow Optimization Algorithm reduces power losses by 49.49% and improves voltage profiles by 85.64% on the IEEE system, outperforming the standard Black Widow Optimization Algorithm (44.81% loss reduction, 84.64% voltage improvement). On the Nigerian network, the Improved Black Widow Optimization Algorithm achieves a 52.86% loss reduction and 92.22% voltage improvement, compared to 25.98% and 79.04% with the Black Widow Optimization Algorithm. These improvements translate to enhanced energy efficiency, reduced technical losses, and better voltage stability, confirming the superior performance of the Improved Black Widow Optimization Algorithm in addressing radial distribution network challenges.
Maintaining Empathy and Relational Integrity in Digitally Mediated Social Work: Practitioner Strategies for Artificial Intelligence Integration Chen, Yih-Chang; Lin, Chia-Ching
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13008

Abstract

This study addresses the critical challenge of preserving relational integrity in social work practice within artificial intelligence (AI)-enhanced environments. While AI technologies promise operational efficiency, their impact on empathy and human connection in social work is not fully understood. This research aims to explore how social workers maintain relational integrity when interacting with clients through AI tools, providing practical strategies and theoretical insights. The research contributes to the field by proposing a relational framework for AI integration in social work practice, emphasizing human-centered principles. The study utilizes a qualitative phenomenological approach, drawing on 24 licensed social workers from diverse sectors (e.g., child welfare, elder care, and mental health) in three urban areas known for AI adoption. Data collection involved semi-structured interviews and artifact analysis, including AI interface screenshots and decision-making protocols, to capture practitioner experiences. Findings reveal three key themes: reframing empathy in digital interactions, AI as a dual partner and adversary, and ethical tensions. Results indicate that video calls and visual aids are crucial for preserving empathy, while social workers employ proactive strategies to manage AI’s limitations. The study highlights the need for clear guidelines, interdisciplinary collaboration, and training to ensure AI supports relational practices rather than replacing them. These findings have significant policy and practice implications, offering a foundation for future research and AI tool development in social services.
Optimal Controller Design of Crowbar System for DFIG-based WT: Applications of Gravitational Search Algorithm Ahmed, Amany Fayz Ali; Elzein, I. M.; Mahmoud, Mohamed Metwally; Ardjoun, Sid Ahmed El Mehdi; Ewias, Ahmed M.; Khaled, Usama
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13027

Abstract

The optimal output and efficacy of a doubly fed induction wind generator (DFIG) are dependent on a multitude of uncontrollable components, necessitating the use of an adequate control system. The crowbar system is essential to the system during abnormal events; thus, it requires appropriate control algorithms and enough control settings. This work suggests the gravitational search algorithm (GSA) to construct the crowbar controller. A synopsis of wind energy and a conversation about the pertinent DFIG component and its methodology. The outcomes acquired with the suggested optimized crowbar system are contrasted with those obtained with a traditional crowbar and without protection. The outcomes confirmed the higher performance of the suggested strategy. The DFIG system responds marginally improved to active and reactive (P&Q) power, DC-Link voltage (DCLV), and machine rotation when a GSA-based PI controller is used. Finally, it can be said that by maintaining the DCLV below the allowable value, which permits the high penetration possibilities of wind energy, the suggested technique assures fault ride-through capacity (FRTC).
Comparative Analysis of PID Tuning Methods for Speed Control in Mecanum-Wheel Electric Wheelchairs Thongpance, Nuntachai; Chotikunnan, Phichitphon; Wongkamhang, Anantasak; Chotikunnan, Rawiphon; Imura, Pariwat; Khotakham, Wanida; Nirapai, Anuchit; Roongprasert, Kittipan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13046

Abstract

This study compares two PID controller tuning methods, particle swarm optimization (PSO) and Cohen-Coon, employed for speed control of an omnidirectional Mecanum-wheel electric wheelchair. Mecanum wheels improve maneuverability on powered mobility platforms; yet, controlling these systems is difficult due to nonlinearities and directional coupling effects. This work investigates the effectiveness of PSO as a sophisticated alternative to traditional PID tuning methods, effectively tackling this issue. This study evaluates P, PI, PD, and PID controllers tuned by both Cohen-Coon and PSO methods, applied to a DC motor system simulating real-world wheelchair actuation. Step response-based system identification models the motor using MATLAB/Simulink. Simulations of a 12V DC motor are examined using controlled-step time-domain inputs. Every controller configuration is subjected to evaluation for overshoot, root mean square error (RMSE), rise time, and settling time. The PSO-tuned PID controller exhibited enhanced performance, characterized by a rise time of 2.06 s, a settling time of 2.37 s, an overshoot of 0.78%, and an RMSE of 4.59, far surpassing the Cohen-Coon variant, which had a settling time of 6.12 s and an overshoot of 20.14%. The results indicate that PSO enhances both transient and steady-state performance in intricate and disturbance-sensitive systems, including Mecanum wheelchairs. Despite PSO's increased computing complexity during offline tuning and the necessity for meticulous parameter selection, its advantages can be precomputed and effectively utilized in real-time embedded systems. This study highlights the importance of safety, dependability, and responsiveness, illustrating that PSO is a scalable and efficient method for improving assistive robotic systems.
Transformer Models in Deep Learning: Foundations, Advances, Challenges and Future Directions Mangkunegara, Iis Setiawan; Purwono, Purwono; Ma’arif, Alfian; Basil, Noorulden; Marhoon, Hamzah M.; Sharkawy, Abdel-Nasser
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13053

Abstract

Transformer models have significantly advanced deep learning by introducing parallel processing and enabling the modeling of long-range dependencies. Despite their performance gains, their high computational and memory demands hinder deployment in resource-constrained environments such as edge devices or real-time systems. This review aims to analyze and compare Transformer architectures by categorizing them into encoder-only, decoder-only, and encoder-decoder variants and examining their applications in natural language processing (NLP), computer vision (CV), and multimodal tasks. Representative models BERT, GPT, T5, ViT, and MobileViT are selected based on architectural diversity and relevance across domains. Core components including self-attention mechanisms, positional encoding schemes, and feed-forward networks are dissected using a systematic review methodology, supported by a visual framework to improve clarity and reproducibility. Performance comparisons are discussed using standard evaluation metrics such as accuracy, F1-score, and Intersection over Union (IoU), with particular attention to trade-offs between computational cost and model effectiveness. Lightweight models like DistilBERT and MobileViT are analyzed for their deployment feasibility. Major challenges including quadratic attention complexity, hardware constraints, and limited generalization are explored alongside solutions such as sparse attention mechanisms, model distillation, and hardware accelerators. Additionally, ethical aspects including fairness, interpretability, and sustainability are critically reviewed in relation to Transformer adoption across sensitive domains. This study offers a domain-spanning overview and proposes practical directions for future research aimed at building scalable, efficient, and ethically aligned. Transformer-based systems suited for mobile, embedded, and healthcare applications.
An Analysis of UAV Security and Privacy Concerns of Communication Systems Hassan, Farah Alaa A.; Almamoori, Hiba Rashid; Al-Msarhed, Nuha Kareem Hameed
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13057

Abstract

Wireless communication is one of the fastest-growing research fields, with Unmanned Aerial Vehicles (UAVs) increasingly deployed as mobile router points in high-traffic areas such as bus stations, metro stations, and airport terminals to address connectivity challenges. However, despite their utility, UAVs face significant security and privacy risks. This paper presents a comprehensive analysis of these vulnerabilities through a systematic four-level classification: sensor, communication, software, and hardware. For each level, we examine (1) common weaknesses exploitable by malicious actors, (2) potential threats to civilian UAV applications, (3) active and passive attacks compromising security and privacy, and (4) possible countermeasures to mitigate such risks. Additionally, we summarize key findings on UAV security and privacy issues and highlight critical unresolved challenges. Finally, we propose future research directions, including the use of fuzzy logic to optimize drone routing by dynamically relocating UAVs to low-activity zones based on fuzzy rule-based decisions.
Enhancement of Channel Estimation in Spectrally Efficient Frequency Division Multiplexing-based Massive MIMO Systems for 5G NR and Beyond: A Comparative Analysis of LSE, MMSE, and Deep Neural Network Architectures Kadhim, Esraa Hadi; Abdulsadda, Ahmad T. Abdulsadda
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13058

Abstract

Channel estimation is a significant challenge in 5G NR and future communication systems because of complicated propagation settings, including high-order modulation and Nakagami-m fading. High spectral efficiency is necessary to satisfy increasing data demands. To improve channel prediction in a 32×32 Massive MIMO architecture, this study suggests a unified framework that combines Deep Neural Networks (DNN), compressed pilot signals, and Spectrally Efficient Frequency Division Multiplexing (SEFDM). The system utilizes SNR as an input characteristic for the deep learning model and 256-QAM modulation. With average MSE (Mean Square Error) values of 1.2776 for LSE (Lest Square Estimation ) and 1.055 for MMSE (Minimum Mean Square Error ), simulation findings show that traditional estimators like LSE and MMSE perform well in moderate-SNR settings. The average MSE of 0.498 obtained by the DNN-based estimator is much lower and best. The model's advantage in capturing nonlinear channel features is shown by graphical comparisons of real vs. anticipated channel gains, leading to better reliability and throughput. In summary, the DNN model exhibits exceptional performance and versatility for real-time channel prediction in spectrally efficient next-generation communication systems, e.g., IoT, autonomous systems.
Induction Motor Speed Control Using PID Tuned by Particle Swarm Optimization Under Vector Control Maghfiroh, Hari; Sulistyo, Meiyanto Eko; Ma’arif, Alfian; Raharja, Nia Maharani; Suwarno, Iswanto; Wati, Dwi Ana Ratna; Baballe, Muhammad Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13112

Abstract

Induction motors (IMs) are widely used in industrial applications due to their cost-effectiveness, durability, and low maintenance requirements. This study investigates the speed control of an induction motor using vector control combined with a PID controller whose parameters are tuned via Particle Swarm Optimization (PID-PSO). A reduced-order small-signal state-space model is derived from a detailed nonlinear IM model to facilitate efficient controller tuning while maintaining fidelity to real-world behavior. The PID parameters are optimized using PSO, with the Integral of Absolute Error (IAE) selected as the objective function due to its ability to penalize long-duration deviations and reflect steady-state performance. The optimized PID controller is then validated on the full nonlinear IM model under speed and load variations. Simulation results demonstrate that PID-PSO significantly outperforms manually tuned PID control in terms of tracking accuracy, reducing IAE by 37.79% and 14.76% under speed and load variation conditions, respectively. However, this improvement comes at the cost of slightly slower settling time. These results highlight a trade-off between accuracy and transient response, motivating future research on multi-objective optimization to balance conflicting criteria such as robustness, energy efficiency, and response time.
Performance Enhancement of Photovoltaic Panels Using Passive Heatsink Cooling and Single-axis Solar Tracking Apribowo, Chico Hermanu Brillianto; Winda, Wiwik Nur; Maghfiroh, Hari; Iftadi, Irwan; Baballe, Muhammad Ahmad
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 2 (2025): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i2.13150

Abstract

Indonesia's persistent tropical climate and strong sunlight year-round lend themselves well to photovoltaic (PV) applications. However, prolonged sun exposure raises panel temperatures and reduces energy conversion efficiency. This study examines how to experimentally enhance the power output and efficiency of PV systems by combining single-axis solar tracking with passive heatsink cooling. On sunny days, two identical 50 W polycrystalline PV panels were evaluated in Surakarta, Indonesia. Four setups were tested: baseline (no tracking or cooling), tracking only, cooling only, and a combination of both. Temperature, voltage, and current data were gathered using calibrated INA219 and MLX90614 sensors. Results indicate the system can enhance efficiency and power output. Tracking alone improved power by 26.42% and efficiency by 2.16%; cooling using an aluminum heatsink boosted power by 40.28% and efficiency by 3.39%. Combining tracking and cooling yielded the highest power increase of 55.61%, with a 2.79% efficiency gain. These findings demonstrate the reduced efficiency benefits due to thermal effects despite higher irradiance in tracking systems. This research offers practical insights for optimizing PV performance in tropical regions and supports developing cost-effective, hybrid enhancement strategies.

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