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All Journal Journal of Moeslim Research Technik Research of Scientia Naturalis Scientechno: Journal of Science and Technology
Muhammad Firdaus Abduh
Universitas Sains dan Teknologi Jayapura
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics

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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.
ARTIFICIAL INTELLIGENCE DRIVEN PREDICTIVE ANALYTICS FOR SUSTAINABLE WATER RESOURCE MANAGEMENT IN RAPIDLY URBANIZING REGIONS ACROSS SOUTHEAST ASIA Luis Santos; Muhammad Firdaus Abduh; Liam Wilson
Research of Scientia Naturalis Vol. 3 No. 3 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientia.v3i3.4115

Abstract

Rapid urbanization in Southeast Asia has exerted unprecedented pressure on water resources, leading to inefficiencies, resource depletion, and challenges in maintaining water quality. Conventional water management approaches often struggle to meet dynamic demand patterns and respond to infrastructure constraints, limiting sustainable urban water governance. This study aims to evaluate the role of artificial intelligence-driven predictive analytics in enhancing water resource management by forecasting demand, detecting system vulnerabilities, and optimizing allocation strategies in rapidly growing urban regions. A mixed-methods research design was employed, integrating quantitative hydrological and consumption datasets with real-time sensor data, machine learning-based predictive modeling, and qualitative expert insights. Data were analyzed through scenario-based simulations, regression analysis, and cross-validation to assess predictive performance and operational effectiveness. Results indicate that AI-enabled predictive analytics significantly reduces non-revenue water from 32% to 19%, improves reservoir stability from 68% to 81%, enhances water quality indices from 74 to 88, and increases leakage detection from 45% to 78%. Case studies demonstrate the practical applicability of predictive alerts in proactive infrastructure management and resource optimization. The study concludes that AI-driven predictive analytics provides a transformative tool for sustainable urban water governance, enabling proactive, efficient, and adaptive management strategies in complex urban environments.
IOT-BASED LOW-COST SENSOR INSTRUMENTATION FOR REAL-TIME WATER QUALITY MONITORING: DESIGN, CALIBRATION, AND FIELD VALIDATION Muhammad Firdaus Abduh; Zain Nizam; Rashid Rahman
Scientechno: Journal of Science and Technology Vol. 5 No. 4 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/scientechno.v5i4.4249

Abstract

Water-quality deterioration requires timely and reliable monitoring, yet conventional laboratory-based approaches often face limitations in cost, sampling frequency, spatial coverage, and response time, particularly in resource-constrained environments. This study aimed to design, calibrate, and validate a low-cost Internet of Things sensor instrumentation system for continuous real-time water-quality monitoring. An experimental engineering design integrated multi-parameter sensing, embedded processing, wireless communication, laboratory calibration, reference-based comparison, field validation, data-transmission assessment, and threshold-based alert evaluation. The results demonstrated strong calibration performance across monitored parameters, although field accuracy varied according to sensor type and environmental conditions. Temperature and pH exhibited comparatively stable performance, whereas turbidity and dissolved oxygen showed greater field-related measurement variability. The system maintained high data completeness and communication reliability while successfully capturing short-duration water-quality changes that periodic sampling could potentially miss. Field validation confirmed that strong laboratory calibration alone did not guarantee equivalent performance under natural environmental conditions. The study concludes that low-cost IoT instrumentation provides a promising fit-for-purpose platform for continuous surveillance, temporal pattern detection, and early warning when supported by rigorous calibration and field validation. The proposed end-to-end framework strengthens the development of scalable, reliable, and context-sensitive water-quality monitoring systems for environmental management.
Co-Authors Anna Schneider James Smith Liam Wilson Luis Santos Rashid Rahman Zain Nizam