Journal of Moeslim Research Technik

AN OPTIMIZED GALLIUM NITRIDE (GAN)-BASED BIDIRECTIONAL DUAL-ACTIVE-BRIDGE CONVERTER FOR HIGH-EFFICIENCY ELECTRIC VEHICLE FAST-CHARGING STATIONS

Sutikno Wahyu Hidayat — 2026-06-29

Rapid expansion of electric vehicle adoption has intensified the demand for high-efficiency fast-charging stations capable of delivering reliable, compact, and bidirectional power conversion while supporting renewable energy integration and vehicle-to-grid applications. Conventional silicon-based converters increasingly face limitations related to switching losses, thermal management, and power density under high-frequency operation. This study aimed to develop and validate an optimized Gallium Nitride (GaN)-based Bidirectional Dual-Active-Bridge (DAB) converter to improve conversion efficiency, thermal performance, and operational flexibility for electric vehicle fast-charging infrastructure. Quantitative engineering research employing mathematical modeling, simulation, optimization, prototype development, and experimental validation was conducted using MATLAB/Simulink, PLECS, and laboratory measurements under representative charging and discharging conditions. Performance indicators included conversion efficiency, switching losses, thermal characteristics, voltage regulation, power density, and dynamic response. Experimental results demonstrated a maximum conversion efficiency of 98.74%, a 51.85% reduction in switching losses, a 73.33% increase in power density, significantly lower operating temperatures, improved voltage regulation, and stable bidirectional power transfer across broad operating conditions. Statistical analysis confirmed significant improvements over conventional silicon-based converter configurations. Integrated optimization of GaN semiconductor devices, adaptive phase-shift modulation, high-frequency transformer design, and digital control collectively produced substantial system-level performance enhancement. Findings indicate that the proposed converter provides a technically robust and energy-efficient solution for next-generation electric vehicle fast-charging stations, supporting sustainable transportation, intelligent energy management, and future smart-grid integration.

DEEP REINFORCEMENT LEARNING FOR DYNAMIC VOLTAGE STABILITY AND FREQUENCY REGULATION IN MICROGRIDS WITH HIGH RENEWABLE ENERGY PENETRATION

Erpan Sahiri — 2026-06-29

The rapid integration of renewable energy into microgrids introduces complex challenges for maintaining dynamic voltage stability and frequency regulation due to the stochastic and intermittent nature of solar and wind generation. Traditional control methods, including PID and model predictive controllers, often fail to adapt effectively to rapid fluctuations and nonlinear system dynamics, highlighting the need for intelligent, adaptive control strategies.This study aims to investigate the effectiveness of deep reinforcement learning (DRL) for real-time voltage stability and frequency regulation in microgrids with high renewable energy penetration. The research seeks to evaluate DRL’s ability to optimize control actions, improve system resilience, and enhance renewable energy utilization compared to conventional methods. A simulation-based approach was employed, modeling microgrid dynamics with integrated solar and wind sources, energy storage systems, and variable loads. DRL controllers were developed using actor-critic architectures and trained to learn optimal control policies through iterative interaction with the simulated environment. System performance was assessed using voltage deviation, frequency deviation, control effort, renewable utilization, and resilience metrics. DRL-based control significantly reduced voltage and frequency deviations to 0.022 p.u. and 0.037 Hz, respectively, while minimizing control effort to 37% and increasing renewable utilization to 92%. System resilience improved to 0.91, outperforming conventional PID and MPC strategies under varying load and generation scenarios. Deep reinforcement learning provides a robust, adaptive approach for microgrid stability management, enabling enhanced reliability, efficiency, and sustainable integration of high-penetration renewable energy. The study demonstrates DRL’s potential for scalable deployment in complex renewable-rich microgrids.

MASSIVE MIMO BEAMFORMING OPTIMIZATION VIA FEDERATED LEARNING FOR ULTRA-RELIABLE LOW-LATENCY COMMUNICATIONS (URLLC) IN 6G NETWORKS

Priyadi Hartoko — 2026-06-29

Rapid development of sixth-generation (6G) wireless networks demands intelligent communication technologies capable of simultaneously achieving ultra-reliable low-latency communications (URLLC), high spectral efficiency, massive connectivity, and privacy-preserving distributed intelligence. Conventional centralized beamforming optimization methods for Massive Multiple-Input Multiple-Output (Massive MIMO) systems often experience excessive communication overhead, scalability limitations, and privacy concerns, reducing their effectiveness in highly dynamic wireless environments. This study aimed to develop and evaluate a Federated Learning-based Massive MIMO beamforming optimization framework that enhances communication reliability, minimizes latency, and improves resource utilization without exchanging raw user data. Quantitative computational research integrating mathematical modeling, distributed machine learning, wireless network simulation, and statistical performance evaluation was conducted using MATLAB, Python, TensorFlow Federated, and NS-3 under heterogeneous 6G communication scenarios. Performance indicators included beamforming accuracy, packet delivery reliability, end-to-end latency, spectral efficiency, signal-to-interference-plus-noise ratio, convergence speed, communication overhead, and energy efficiency. Experimental results demonstrated beamforming accuracy of 98.43%, packet delivery reliability of 99.998%, average latency of 0.74 ms, significant improvements in spectral and energy efficiency, accelerated model convergence, and substantially reduced communication overhead compared with centralized optimization approaches. Findings confirm that integrating Federated Learning with Massive MIMO beamforming provides a scalable, privacy-preserving, and highly efficient optimization framework capable of supporting future AI-native 6G networks and mission-critical wireless communication services.

EMBEDDED INTELLIGENCE: EDGE COMPUTING ARCHITECTURES FOR REAL-TIME CONTROL APPLICATIONS

Sayed Achmady — 2026-06-28

Real-time control applications demand low-latency, reliable, and energy-efficient computational frameworks. Traditional cloud-centric architectures often fail to meet these requirements due to network-induced delays, unpredictable bandwidth, and limited adaptability under dynamic workloads. The integration of embedded intelligence within edge computing environments has emerged as a promising solution to enhance responsiveness, operational reliability, and system scalability. This research investigates edge computing architectures designed for embedded intelligence, aiming to optimize latency, throughput, and energy consumption in heterogeneous hardware configurations. Experimental and simulation-based methods were employed to evaluate performance across microcontrollers, FPGAs, and CPU/GPU nodes under varying workloads and network conditions. Data collection included latency measurements, throughput analysis, task completion times, and energy profiling. Inferential analyses, including correlation and regression models, quantified the relationship between computational capacity, responsiveness, and efficiency. A robotic manipulation case study further validated the practical application of the proposed architectures. Results indicate that adaptive, edge-enabled embedded intelligence significantly reduces latency to sub-10 millisecond levels, maintains high throughput, and ensures consistent task completion under dynamic conditions. Heterogeneous architectures outperform uniform deployments in both reliability and energy-performance balance. These findings demonstrate the feasibility and effectiveness of integrating embedded intelligence at the edge for real-time control. The study provides actionable guidance for designing scalable, robust, and energy-efficient intelligent control systems.

HUMAN–MACHINE INTERACTION IN ENGINEERING SYSTEMS: CONTROL, COGNITION, AND SYSTEM INTEGRATION

Joni Wilson Sitopu — 2026-06-30

Higher education institutions are increasingly expected to produce graduates who possess not only academic competence but also social responsibility, civic engagement, and the ability to address complex community challenges. Service learning has emerged as a transformative pedagogical approach that integrates academic learning with meaningful community service, enabling students to connect theoretical knowledge with real-world experiences. Growing emphasis on experiential and community-based learning has intensified interest in understanding the educational value and broader impact of service learning within higher education contexts. This study aims to examine the integration of service learning in higher education and evaluate its contribution to student learning outcomes, civic development, and community engagement. A qualitative research design based on systematic literature review and thematic analysis was employed. Data were collected from peer-reviewed journal articles, institutional reports, policy documents, and educational studies published between 2015 and 2025. Findings indicate that service learning significantly enhances critical thinking, problem-solving skills, communication abilities, social awareness, and civic responsibility among students. Meaningful collaboration between universities and community partners also contributes to reciprocal benefits, including community empowerment and the development of sustainable social initiatives. Institutional support, curriculum alignment, reflective learning practices, and stakeholder collaboration emerged as key factors influencing successful implementation. The study concludes that integrating service learning into higher education strengthens the connection between academic knowledge and social engagement, fostering holistic student development while promoting universities’ contributions to community well-being and sustainable societal development.

ENERGY-EFFICIENT POWER ELECTRONICS: DESIGN STRATEGIES FOR SUSTAINABLE ELECTRICAL ENGINEERING

Muhammad Firdaus Abduh — 2026-06-24

Increasing global energy demand, rapid electrification, and growing environmental concerns have intensified the need for energy-efficient technologies capable of supporting sustainable development. Power electronics plays a crucial role in modern electrical engineering by enabling efficient energy conversion, transmission, and utilization across renewable energy systems, electric vehicles, smart grids, and industrial applications. Persistent challenges related to switching losses, thermal dissipation, and converter inefficiencies continue to limit overall system performance and sustainability outcomes. This study aims to examine design strategies that enhance energy efficiency in power electronic systems and to evaluate their contribution to sustainable electrical engineering. A qualitative literature-based research design employing a systematic review approach was adopted. Relevant peer-reviewed publications published between 2015 and 2025 were analyzed to identify emerging technological trends, efficiency-enhancing mechanisms, and sustainability-oriented design principles. Findings indicate that advanced semiconductor technologies, particularly silicon carbide (SiC) and gallium nitride (GaN), significantly reduce power losses and improve conversion efficiency. Optimized converter topologies, intelligent control algorithms, and advanced thermal management systems further enhance system reliability and operational performance. Integrated implementation of these strategies produces greater efficiency gains than isolated technological improvements. The study concludes that sustainable electrical engineering requires a holistic design framework that combines technological innovation, system optimization, and environmental considerations. Such an approach can accelerate the development of highly efficient, reliable, and environmentally responsible electrical energy systems.

STRUCTURAL OPTIMIZATION UNDER EXTREME CONDITIONS: ENGINEERING DESIGN FOR CLIMATE-INDUCED HAZARDS

Saripuddin M — 2026-06-30

Structural infrastructure worldwide faces increasing exposure to climate-induced hazards, including extreme flooding, high-intensity wind events, prolonged heat waves, and compound environmental stressors that challenge conventional engineering design standards. Growing uncertainty associated with climate change necessitates innovative approaches capable of enhancing resilience while maintaining structural efficiency and economic feasibility. This study aims to examine the effectiveness of structural optimization strategies in improving infrastructure performance under extreme environmental conditions. A quantitative engineering research design was employed using finite element modeling, climate hazard simulations, probabilistic risk assessment, and multi-objective optimization techniques. Structural systems were evaluated across multiple hazard scenarios to assess resilience, reliability, material efficiency, failure probability, and lifecycle cost performance. Results indicate that optimized structures achieved significantly higher resilience scores, improved structural reliability, reduced stress concentrations, lower failure probabilities, and greater material efficiency compared with conventional designs. Optimization-based configurations demonstrated superior adaptability to future climate scenarios and maintained operational performance under severe loading conditions. Case-study simulations further revealed substantial reductions in displacement and maintenance requirements while improving long-term infrastructure sustainability. Findings suggest that integrating climate projections with advanced optimization frameworks can substantially strengthen engineering resilience and support more effective adaptation strategies. Structural optimization therefore represents a promising pathway for developing safer, more sustainable, and climate-responsive infrastructure systems capable of addressing emerging environmental risks.