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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 4 Documents
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Search results for , issue "Vol. 3 No. 1 (2026)" : 4 Documents clear
REENGINEERING STRUCTURAL RESILIENCE: A MULTISCALE FRAMEWORK FOR PERFORMANCE-BASED INFRASTRUCTURE DESIGN Manurung, Edison Hatoguan; Suryawan, M. Alit; Manurung, Hotasi Rogate; Takahashi, Haruto
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 Tri Ika R., Aris
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 Hartoko, Priyadi; Farizal, Ahmad
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 Ahmad Fawzy Muntasir, Nabiyl
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.

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