Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Journal of Moeslim Research Technik Journal of Selvicoltura Asean Journal of Tecnologia Quantica
Siti Mariam
Universiti Teknologi Brunei (UTB)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Control & Systems Engineering Electrical & Electronics Engineering Engineering Physics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
TECHNO-ECONOMIC ANALYSIS OF A COMMUNITY-OWNED RENEWABLE ENERGY COOPERATIVE BASED ON A WAQF (ISLAMIC ENDOWMENT) MODEL Siti Mariam; Ayesha Begum; Pieter Hendriks
Journal of Moeslim Research Technik Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v2i4.2717

Abstract

The global energy transition requires novel, equitable financing models for decentralized, community-owned renewable energy (CORE) systems, as high-cost conventional capital often renders essential infrastructure projects financially unviable in developing regions. This study aims to develop and validate a bespoke Techno-Economic Model (TEM) that quantifies the structural benefits of integrating a CORE cooperative with the perpetual, non-profit Waqf (Islamic Endowment) financing mechanism. A quantitative approach utilized the TEM to optimize a 25-year microgrid project lifespan, comparing a Waqf-funded scenario (zero cost of capital, 30% mandatory asset preservation fund) against a Conventional Debt Benchmark (CDB) with an 8.5% interest rate. The optimized 250 kWp PV/500 kWh BESS Waqf-CORE system achieved a Levelized Cost of Energy (LCOE) of 0.081/kWh, which is 35.2% lower than the CDB’s LCOE of 0.125/kWh. This cost reduction equated to a 1.83 million capital avoidance over the project’s Net Present Cost (NPC). The Waqf model fundamentally eliminates debt-related overheads and ensures perpetual asset maintenance, proving that patient, ethical capital is structurally superior for long-duration public utility infrastructure. This offers a robust, scalable, and self-sustaining blueprint for achieving energy access and climate resilience in Muslim-majority nations and beyond.
AI-POWERED PREDICTIVE MODELING OF FOREST FIRE RISK IN RIAU PROVINCE BASED ON CLIMATE, PEATLAND, AND LAND USE DATA Loso Judijanto; Siti Mariam; Ahmad Zainal
Journal of Selvicoltura Asean Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsa.v2i3.2484

Abstract

Forest and peatland fires in Riau Province, Indonesia, are a recurrent environmental disaster with severe regional and global consequences. Traditional fire danger rating systems often fail to capture the complex interplay of factors driving these events. The advancement of artificial intelligence (AI) offers an opportunity to develop more accurate and dynamic fire risk prediction models. This study aimed to develop and validate a high-performance, AI-powered model for predicting daily forest fire risk at a high spatial resolution across Riau Province by integrating climate, peatland, and land use data. We integrated historical satellite-detected fire hotspots (2015-2023) as the dependent variable. Predictor variables included daily climate data (e.g., temperature, precipitation, wind speed), static peatland characteristics (e.g., depth, type), and dynamic land use/land cover data. An XGBoost (Extreme Gradient Boosting) machine learning algorithm was trained to learn the complex, non-linear relationships between these drivers and fire occurrence. The model’s predictive performance was rigorously evaluated using the Area Under the Curve (AUC) metric. The XGBoost model demonstrated high predictive accuracy, achieving an AUC of 0.93. The analysis revealed that the number of consecutive dry days, peatland depth, and proximity to oil palm plantations were the most influential variables in predicting fire risk. The model successfully generated daily 1-km resolution fire risk maps, identifying specific areas with elevated danger. The AI-powered model provides a robust and significantly more accurate tool for forest fire forecasting in fire-prone tropical peatland landscapes. This approach offers a critical advancement for developing effective early warning systems, enabling targeted resource allocation for fire prevention and mitigation efforts.
Quantum Computing Algorithms for Optimization Problems: A Study Based on QAOA and MAX-CUT Simulation Ndiana Okon Asuquo; Siti Mariam
Journal of Tecnologia Quantica Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i2.3479

Abstract

Quantum computing represents a transformative computational paradigm capable of solving certainoptimization problems more efficiently than classical algorithms. Many real?world applicationsincluding logistics planning, transportation routing, network optimization, and machine learninginvolve combinatorial problems whose computational complexity grows exponentially with input size.This research investigates quantum algorithms for solving optimization problems, with emphasis onthe Quantum Approximate Optimization Algorithm (QAOA), Grover search techniques, andHamiltonian?based optimization frameworks. Mathematical formulations are developed for representingclassical optimization problems using quantum Hamiltonians, enabling their implementation inparameterized quantum circuits. Simulation experiments based on the MAX?CUT problem are conducted toevaluate algorithm performance. Benchmark comparisons between classical and quantum optimizationapproaches demonstrate improved scalability for quantum algorithms in simulated environments. Theresults suggest that hybrid quantum?classical optimization methods may offer practical advantagesfor solving medium?scale combinatorial problems on near?term quantum hardware. Keywords: Quantum Computing; QAOA; Combinatorial Optimization; MAX?CUT; Quantum Algorithms; Quantum Annealing
Co-Authors Ahmad Zainal Ayesha Begum Loso Judijanto Ndiana Okon Asuquo Pieter Hendriks