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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 84 Documents
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ALGORITHMIC SHARIA COMPLIANCE: A MACHINE LEARNING FRAMEWORK FOR AUTOMATED RISK ASSESSMENT AND SCREENING IN ISLAMIC FINTECH Nurul Ain Safrizon; Vugar Abdullayev
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.v3i2.4129

Abstract

Rapid expansion of Islamic Financial Technology (Islamic FinTech) has increased the complexity of ensuring continuous Sharia compliance across digital financial products, investment services, and automated transactions. Conventional compliance assessment primarily depends on manual evaluation by Sharia scholars and supervisory boards, creating challenges related to scalability, consistency, operational efficiency, and timely risk identification. This study aimed to develop and evaluate a machine learning framework for automated Sharia compliance assessment and financial risk screening that integrates predictive intelligence with explainable and transparent decision support. A mixed-methods sequential explanatory research design was employed, combining quantitative analysis of 125,000 anonymized financial transaction records using supervised machine learning algorithms with qualitative evidence obtained from expert interviews, institutional document analysis, and regulatory validation. Comparative evaluation demonstrated that the proposed framework achieved high predictive performance, with XGBoost providing the highest classification accuracy while Explainable Artificial Intelligence techniques enhanced transparency through interpretable decision explanations. Qualitative findings confirmed that automated screening significantly reduced compliance review time, strengthened institutional consistency, and improved stakeholder confidence without replacing the essential role of Sharia scholars in complex jurisprudential decisions. Results indicate that algorithmic Sharia compliance functions most effectively as a human-centered decision-support framework integrating machine learning, Islamic jurisprudence, financial governance, and explainable artificial intelligence. Responsible implementation of this framework provides a scalable pathway toward trustworthy Islamic FinTech, enhanced regulatory accountability, and sustainable digital financial innovation.
ADVANCED SENSOR FUSION: SIGNAL PROCESSING ARCHITECTURES FOR AUTONOMOUS ENGINEERING PLATFORMS Justam Justam; Liu Yang; Chen Mei
Journal of Moeslim Research Technik Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i4.4323

Abstract

The reliability of autonomous engineering platforms depends fundamentally on the seamless integration of high-bandwidth, multi-modal sensory data to perceive dynamic environments. This research addresses the persistent challenge of computational latency and perception failure in traditional sensor fusion architectures when operating under adverse conditions. The study aims to evaluate a novel hybrid signal processing architecture that optimizes the balance between edge-level feature extraction and centralized semantic synthesis. Utilizing a “Hardware-in-the-Loop” methodology, the proposed framework was tested on an embedded GPU testbed using synchronized LiDAR, RADAR, and camera datasets across 500 diverse navigational scenarios. Results demonstrate that the hybrid architecture achieves a 56% reduction in processing latency, maintaining a mean response time of 12.4 milliseconds without compromising positional accuracy, which remained stable at 0.11 meters RMSE. Furthermore, the implementation of an entropy-driven weighting mechanism allowed the system to maintain 99.2% anomaly detection accuracy during simulated sensor failures. This research concludes that decentralized feature processing is essential for the operational continuity of energy-constrained autonomous systems. The findings provide a scalable blueprint for developing resilient, low-power perception modules, asserting that hardware-aware signal processing is a prerequisite for achieving Level 5 autonomy in complex, real-world engineering applications.
FLUID-STRUCTURE INTERACTION ANALYSIS OF ENERGY-SAVING DEVICES (ESDS) FOR ENHANCING PROPULSIVE EFFICIENCY IN LARGE COMMERCIAL VESSELS Ardiansyah Ardiansyah
Journal of Moeslim Research Technik Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i4.4300

Abstract

Large commercial vessels require substantial propulsion power, while conventional rigid-body analyses may overestimate the effectiveness of energy-saving devices by neglecting structural deformation under unsteady hydrodynamic loading. This study aimed to evaluate the coupled hydrodynamic and structural performance of selected energy-saving devices and determine their contribution to propulsive-efficiency improvement. A computational–experimental multiphysics approach integrated computational fluid dynamics, finite-element analysis, one-way and two-way fluid–structure interaction, modal and fatigue assessment, model-scale validation, off-design testing, and multi-objective optimization. Results showed that the selected hybrid device improved wake uniformity, weakened residual rotational flow, reduced hull-pressure fluctuations, and lowered delivered power by 7.31% at the design condition. Rigid CFD predicted an 8.20% reduction, indicating that structural flexibility caused a measurable loss of hydrodynamic benefit. Maximum deformation reached 18.6 mm, while local reinforcement reduced stress concentration and extended the estimated fatigue life from 11.8 to 22.6 years. Performance declined under slow-steaming, high-speed, and ballast conditions because of wake mismatch, increased appendage resistance, and greater deformation. The study concludes that reliable ESD design requires two-way hydroelastic assessment integrating efficiency, structural safety, vibration, fatigue resistance, and operational robustness across realistic vessel conditions. This integrated framework supports more accurate retrofit decisions and more credible fuel-saving projections for commercial shipping operations.
THERMO-ECONOMIC AND EMISSION ASSESSMENT OF AMMONIA-HYDROGEN DUAL-FUEL COMBUSTION IN ADVANCED INTERNAL COMBUSTION ENGINES FOR DECARBONIZED POWER GENERATION Dedy Aryanto
Journal of Moeslim Research Technik Vol. 3 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v3i4.4438

Abstract

Ammonia–hydrogen dual-fuel combustion offers a promising pathway for dispatchable low-carbon power generation, yet its practical viability depends on balancing efficiency, cost, and nitrogen-based emissions. This study aimed to evaluate the thermodynamic, economic, and environmental performance of ammonia–hydrogen combustion in an advanced internal combustion engine. A quantitative experimental design combined single-cylinder engine testing, thermo-economic modeling, life-cycle emission assessment, statistical analysis, and multi-objective optimization. Hydrogen energy fractions from 0% to 40% were examined across multiple loads and ignition settings, with selected tests incorporating exhaust-gas recirculation. Results showed that hydrogen enrichment shortened ignition delay, improved combustion stability, increased brake thermal efficiency, and reduced ammonia slip and nitrous oxide emissions. Hydrogen fractions between 20% and 30% provided the most balanced performance, while 40% enrichment produced only marginal efficiency gains and higher nitrogen-oxide emissions and electricity costs. A 30% hydrogen fraction with 10% exhaust-gas recirculation maintained 37.60% brake thermal efficiency and reduced nitrogen oxides by more than 40%. Renewable fuel pathways achieved the lowest life-cycle emissions but the highest levelized electricity cost. The study concludes that coordinated optimization of fuel composition, combustion control, emission mitigation, and fuel-production pathways is essential for credible decarbonized power generation under technically stable and economically plausible operating conditions at scale.

Page 9 of 9 | Total Record : 84

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