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 7 Documents
 1 
Search results for , issue "vol. 3 no. 1 (2026)" : 7 Documents clear
REENGINEERING STRUCTURAL RESILIENCE: A MULTISCALE FRAMEWORK FOR PERFORMANCE-BASED INFRASTRUCTURE DESIGN Edison Hatoguan Manurung; M. Alit Suryawan; Hotasi Rogate Manurung; Haruto Takahashi
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.v3i1.3295

Abstract

Increasing exposure of infrastructure systems to extreme hazards, aging effects, and climate-induced uncertainties has revealed fundamental limitations of conventional strength- and safety-oriented design approaches. Structural performance can no longer be evaluated solely in terms of damage prevention, but must also account for functionality loss, system interdependencies, and recovery capacity. This study aims to reengineer the concept of structural resilience by developing a multiscale framework that integrates resilience explicitly into performance-based infrastructure design. The research adopts an analytical and framework-oriented methodology, combining critical synthesis of performance-based design theories, structural resilience metrics, and systems engineering concepts. Multiscale linkages are established among component-level behavior, system-level functionality, and network-level performance, with explicit consideration of temporal recovery processes. The results demonstrate that resilience is an emergent and time-dependent system property that cannot be inferred directly from component-level performance indicators. Local strengthening strategies are shown to yield limited resilience gains unless supported by system redundancy, connectivity, and recovery-oriented design. The proposed framework reveals hidden vulnerabilities and recovery bottlenecks that remain unaddressed in conventional performance-based approaches. The study concludes that effective resilience-oriented infrastructure design requires a paradigm shift toward multiscale, system-aware, and recovery-informed performance objectives. Embedding these principles into performance-based design provides a robust foundation for enhancing infrastructure reliability, functionality, and societal resilience under extreme and uncertain conditions.
FROM MATERIALS TO MECHANISMS: MULTIPHYSICS OPTIMIZATION IN NEXT-GENERATION MECHANICAL ENGINEERING Aris Tri Ika R.
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.v3i1.3435

Abstract

Increasing performance demands in aerospace, energy, and advanced manufacturing systems require mechanical designs capable of operating under strongly coupled thermal, mechanical, fluidic, and electromagnetic conditions. Conventional single-physics optimization approaches are insufficient to capture nonlinear interactions that govern durability, efficiency, and structural stability in next-generation engineering systems. This study aims to develop an integrated multiphysics optimization framework that bridges material-level constitutive behavior with mechanism-level system performance. A computational research design was employed, combining physics-based multiphysics modeling, finite element analysis, computational fluid dynamics, and multi-objective optimization algorithms within a unified architecture. Temperature-dependent and nonlinear material properties were dynamically updated during iterative optimization cycles. Physics-informed surrogate modeling was incorporated to accelerate convergence while maintaining predictive reliability. Three representative case systems were evaluated to validate the proposed framework. Results indicate significant improvements in structural and energetic performance, including reductions in peak stress and thermal gradients, enhanced fatigue life, improved vibration stability, and increased energy efficiency. Statistical analysis confirmed the robustness and practical significance of these improvements. The study concludes that mechanism-centered multiphysics optimization represents a critical advancement beyond conventional sequential design strategies, offering a scalable and reliable pathway for developing resilient, high-performance mechanical systems.
OPTIMIZATION OF ENERGY MANAGEMENT STRATEGY BASED ON FUZZY LOGIC FOR HYBRID ELECTRIC VEHICLE ELECTRONIC CONTROL SYSTEM Priyadi Hartoko; Ahmad Farizal
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.v3i1.3444

Abstract

The growing demand for sustainable transportation solutions has led to significant advancements in hybrid electric vehicles (HEVs). However, optimizing energy management in these systems remains a critical challenge. This study explores the application of fuzzy logic-based energy management strategies to optimize the performance of hybrid electric vehicles. The primary aim is to develop a real-time adaptive system capable of improving energy efficiency and reducing CO2 emissions by optimizing power distribution between the internal combustion engine and the electric motor. The research employs a quantitative approach, using both simulations and real-world testing of selected HEV models. Data on energy consumption and CO2 emissions were collected and analyzed across various driving cycles. The results indicate that the fuzzy logic-based energy management system significantly reduced energy consumption by up to 21.6% and CO2 emissions by 22.2% compared to traditional energy management systems. The fuzzy logic system demonstrated superior adaptability to dynamic driving conditions, leading to enhanced vehicle performance and sustainability. This study concludes that fuzzy logic offers a robust solution for optimizing energy management in hybrid vehicles, contributing to reduced fuel consumption and environmental impact. Future research should focus on integrating machine learning techniques and expanding the system’s application to a wider range of hybrid and electric vehicle models.
OPTIMIZING MAXIMUM POWER POINT TRACKING (MPPT) USING DEEP REINFORCEMENT LEARNING TO IMPROVE SOLAR PANEL EFFICIENCY UNDER DYNAMIC WEATHER CONDITIONS Nabiyl Ahmad Fawzy Muntasir
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.v3i1.3470

Abstract

Solar photovoltaic (PV) systems play an important role in the transition toward sustainable energy. Variations in solar irradiance, temperature, and partial shading caused by dynamic weather conditions often reduce the efficiency of photovoltaic power generation. Conventional Maximum Power Point Tracking (MPPT) algorithms such as Perturb and Observe and Incremental Conductance frequently experience difficulties in maintaining the global maximum power point when environmental conditions change rapidly. Intelligent control approaches are therefore required to improve the adaptability and performance of MPPT systems. This study aims to develop and evaluate an MPPT optimization method based on Deep Reinforcement Learning (DRL) to enhance solar panel efficiency under dynamic weather conditions. The proposed method is designed to enable the controller to learn optimal operating strategies through continuous interaction with the photovoltaic system environment. A quantitative experimental design was implemented using a photovoltaic simulation model integrated with a DC–DC boost converter and a DRL-based control framework. Environmental scenarios including fluctuating irradiance and temperature variations were simulated to evaluate system performance. The DRL-based MPPT algorithm was compared with conventional MPPT techniques using metrics such as tracking efficiency, convergence speed, and power stability. Results show that the proposed DRL-based MPPT method achieved higher tracking efficiency (98.3%), faster convergence time, and improved power stability under dynamic weather conditions compared with traditional algorithms. These findings indicate that reinforcement learning provides a robust and adaptive solution for optimizing photovoltaic power extraction. The study concludes that Deep Reinforcement Learning can significantly enhance MPPT performance and support the development of intelligent photovoltaic energy systems capable of operating efficiently in highly variable environmental conditions.
ELECTROCHEMICAL SYSTEMS ENGINEERING: MODELING, CONTROL, AND DEGRADATION ANALYSIS Yui Nakamura; Li Wei; Arfan Arfan
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.v3i1.3341

Abstract

The global transition toward sustainable energy infrastructure relies heavily on the reliability and longevity of electrochemical energy storage systems. However, conventional management strategies often struggle with the highly non-linear dynamics and unobservable internal degradation mechanisms of these devices. This research addresses the critical need for advanced systems engineering by evaluating a physics-based framework for real-time modeling, state-aware control, and non-invasive degradation analysis. The study aims to optimize the balance between operational performance and capacity retention through the implementation of reduced-order Doyle-Fuller-Newman models. Utilizing a multi-physics experimental design, forty lithium-ion cells were subjected to high-rate cycling while monitored by an adaptive observer-based controller. Results demonstrate that the physics-based approach achieves a 75% reduction in state-of-estimation error compared to empirical models, while significantly mitigating internal resistance growth. Furthermore, the “health-aware” control strategy successfully improved capacity retention by 7.2% over 1,000 cycles by preemptively preventing lithium plating thresholds. This research concludes that internal state visibility is a prerequisite for achieving maximum electrochemical utilization. The findings provide a scalable blueprint for the next generation of resilient battery management systems, asserting that the integration of multi-scale physical models into control architectures is essential for securing the future of global energy storage.
ELECTROMECHANICAL SYNERGY: ADVANCED MODELING OF COUPLED ELECTRICAL–MECHANICAL ENERGY SYSTEMS Rusman Rusman; Ava Lee; Sarah Williams
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.v3i1.3375

Abstract

Electromechanical energy systems increasingly underpin modern transportation, industrial automation, and renewable power technologies, yet their behavior is often modeled through simplified decoupled approaches that overlook dynamic interaction between electrical and mechanical domains. This study aims to develop and evaluate an advanced modeling framework that explicitly represents electromechanical synergy as a bidirectional and nonlinear energy exchange process. The research employs a quantitative, model-driven methodology integrating electromagnetic equations, mechanical dynamics, and coupling coefficients within a unified simulation environment. Representative electromechanical systems are analyzed under steady-state and transient operating conditions to assess model accuracy and system behavior. The results demonstrate that coupled modeling significantly improves predictive accuracy for torque response, angular velocity, vibration behavior, and system stability compared to conventional decoupled models. The findings also reveal that coupling effects intensify during transient excitation and load variation, confirming the central role of interaction dynamics in system performance. The study concludes that electromechanical systems should be treated as integrated energy structures rather than isolated subsystems. Advanced coupled modeling provides a robust analytical foundation for design optimization, control development, and reliability assessment in complex energy systems. These contributions support future interdisciplinary research and facilitate practical implementation across emerging electromechanical applications worldwide in diverse industrial academic settings.
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.

Page 1 of 1 | Total Record : 7

 1 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 4 (2026) 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