cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Adam Mudinillah
Contact Email
adammudinillah@staialhikmahpariangan.ac.id
Phone
+6285379388533
Journal Mail Official
adammudinillah@staialhikmahpariangan.ac.id
Editorial Address
Jorong Kubang Kaciak Dusun Kubang Kaciak, Kelurahan Balai Tangah, Kecamatan Lintau Buo Utara, Kabupaten Tanah Datar, Provinsi Sumatera Barat, Kodepos 27293.
Location
Kab. tanah datar,
Sumatera barat
INDONESIA
Journal of Moeslim Research Technik
Published by Yayasan Adra Karima Hubbi
ISSN : 30476704     EISSN : 30476690     DOI : 10.70177/technik
Core Subject : Engineering,
Aerospace Engineering Automotive Engineering Control & Systems Engineering Electrical & Electronics Engineering Engineering
Journal of Moeslim Research Technik is is a Bimonthly, open-access, peer-reviewed publication that publishes both original research articles and reviews in all fields of Engineering including Civil, Mechanical, Industrial, Electrical, Computer, Chemical, Petroleum, Aerospace, Architectural, etc. It uses an entirely open-access publishing methodology that permits free, open, and universal access to its published information. Scientists are urged to disclose their theoretical and experimental work along with all pertinent methodological information. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 81 Documents
 5   6   7   8   9 
OPTIMIZING MAXIMUM POWER POINT TRACKER (MPPT) USING HYBRID CUCKOO SEARCH-PSO ALGORITHM ON SOLAR ENERGY CONVERSION SYSTEM UNDER PARTIAL SHADING CONDITIONS Erpan Sahiri
Journal of Moeslim Research Technik Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i1.3459

Abstract

The efficiency of solar energy systems is highly dependent on the accurate tracking of the maximum power point (MPP), especially under partial shading conditions, which are common in real-world environments. Traditional Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe (P&O) and Incremental Conductance (IncCond) often fail to track the global MPP under such conditions, resulting in significant energy loss. This study presents a hybrid optimization approach using the Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms to improve the accuracy and speed of MPP tracking in solar energy systems under partial shading. The primary objective is to evaluate the effectiveness of the hybrid Cuckoo Search-PSO (CS-PSO) algorithm compared to conventional MPPT methods. A simulation-based approach was employed to model the solar energy conversion system and assess the performance of the MPPT algorithms. The results show that the CS-PSO algorithm outperforms traditional methods, achieving a tracking accuracy of 98.4%, with a reduced time to reach the MPP (8.7 seconds). In contrast, P&O and IncCond exhibited lower accuracy and slower convergence times. The study concludes that the hybrid CS-PSO algorithm provides a more efficient solution for optimizing MPPT under partial shading conditions, offering significant improvements in energy efficiency and tracking performance.
ARCHITECTURAL ENGINEERING IN THE DIGITAL ERA: PARAMETRIC DESIGN AND STRUCTURAL RATIONALIZATION Veronika Widi Prabawasari; Haruto Takahashi; Faizal Baharuddin; Anna Schneider
Journal of Moeslim Research Technik Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i2.3624

Abstract

Architectural engineering in the digital era is increasingly shaped by parametric design methodologies that enable complex form generation and performance-driven optimization. Rapid advancements in computational tools have transformed design processes, yet a persistent gap remains between architectural exploration and structural rationalization, often resulting in inefficiencies and post-design adjustments. This study aims to develop an integrated computational framework that aligns parametric design with structural performance, ensuring that architectural forms are both innovative and structurally feasible. A computational design-based methodology was employed, combining parametric modeling, finite element analysis, and algorithmic optimization across representative architectural typologies. Iterative workflows were implemented to establish continuous feedback between geometric parameters and structural responses. Results indicate that integrated parametric-structural models achieve higher structural efficiency, reduced material consumption, and improved deformation control compared to conventional and non-integrated approaches. Statistical analysis confirms significant performance improvements, while case-based validation demonstrates strong alignment between simulated and expected structural behavior. Findings further reveal that real-time integration enhances design adaptability and decision-making efficiency. This study concludes that the integration of parametric design and structural rationalization represents a robust and scalable paradigm for contemporary architectural engineering, offering significant implications for sustainability, performance optimization, and interdisciplinary collaboration.
BEYOND DETERMINISTIC MODELS: PROBABILISTIC APPROACHES TO RISK-AWARE CIVIL ENGINEERING SYSTEMS Joni Wilson Sitopu; Virgo Erlando Purba; Dermina Roni Santika Damanik; Sarah Williams
Journal of Moeslim Research Technik Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i2.3625

Abstract

Civil engineering systems increasingly operate under conditions of uncertainty, variability, and exposure to extreme events, challenging the adequacy of deterministic modeling approaches that rely on fixed assumptions and simplified safety margins. Probabilistic methods offer a more realistic representation by explicitly incorporating uncertainty into analysis and decision-making processes. This study aims to develop a risk-aware probabilistic framework that enhances reliability assessment and supports more informed engineering decisions. A mixed-methods computational design was employed, integrating stochastic modeling, Monte Carlo simulation, Bayesian updating, and reliability analysis across representative infrastructure systems. Results indicate that probabilistic and hybrid models achieve higher reliability indices, lower probabilities of failure, and reduced expected losses compared to deterministic approaches. Statistical analysis confirms significant differences in performance, while case-based validation demonstrates strong agreement between probabilistic predictions and observed system behavior. Findings further reveal that adaptive integration of data-driven techniques improves model accuracy and responsiveness under dynamic conditions. This study concludes that probabilistic approaches provide a robust and scalable paradigm for risk-aware civil engineering, offering substantial implications for infrastructure design, maintenance, and resilience planning.
EMBEDDED INTELLIGENCE: EDGE COMPUTING ARCHITECTURES FOR REAL-TIME CONTROL APPLICATIONS Sayed Achmady; Nopriadi Nopriadi; Ahmad Ikhwan; Sun Wei
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.3898

Abstract

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; Darwan Edyanto Saragih; Haruto Takahashi
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.3949

Abstract

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; Anna Schneider; James Smith
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.3985

Abstract

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; Ethan Tan; Giovanni Rossi
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.4004

Abstract

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.
AN OPTIMIZED GALLIUM NITRIDE (GAN)-BASED BIDIRECTIONAL DUAL-ACTIVE-BRIDGE CONVERTER FOR HIGH-EFFICIENCY ELECTRIC VEHICLE FAST-CHARGING STATIONS Sutikno Wahyu Hidayat; Ferdy Hendarto; Pompy Pratisna
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.4093

Abstract

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
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.4095

Abstract

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; Ahmad Farizal
Journal of Moeslim Research Technik Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i3.4097

Abstract

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.

Page 8 of 9 | Total Record : 81

 5   6   7   8   9 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 3 (2026) Vol. 3 No. 2 (2026) Vol. 3 No. 1 (2026) Vol. 2 No. 6 (2025) Vol. 2 No. 5 (2025) Vol. 2 No. 4 (2025) Vol. 2 No. 3 (2025) Vol. 2 No. 2 (2025) Vol. 2 No. 1 (2025) Vol. 1 No. 6 (2024) Vol. 1 No. 5 (2024) Vol. 1 No. 4 (2024) Vol. 1 No. 3 (2024) Vol. 1 No. 2 (2024) Vol. 1 No. 1 (2024) More Issue