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All Journal Rekayasa Sipil JURNAL SAINTIS Journal of Railway Transportation and Technology Journal of Future Artificial Intelligence and Technologies Jurnal Teknik Sipil dan Perencanaan
Fuqaha, Sameh
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Artificial Intelligence and IoT for Smart Waste Management: Challenges, Opportunities, and Future Directions Fuqaha, Sameh; Nursetiawan, Nursetiawan
Journal of Future Artificial Intelligence and Technologies Vol. 2 No. 1 (2025): in progress
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-85

Abstract

Indonesia’s waste management system struggles to keep pace with rapid population growth and urbanization, resulting in inefficient waste collection, environmental degradation, and low recycling rates. The country predominantly relies on open dumping and landfilling, which contribute significantly to pollution and greenhouse gas emissions. This study explores the transformative role of Artificial Intelligence (AI) and the Internet of Things (IoT) in waste management, focusing on smart waste collection, automated sorting, real-time monitoring, and predictive analytics. AI-driven waste classification enhances recycling efficiency, while IoT-enabled smart bins optimize collection routes, reducing operational costs and landfill dependency. Despite these advantages, challenges such as high implementation costs, digital infrastructure limitations, and data privacy concerns hinder widespread adoption. This study highlights that policy support, investment in digital infrastructure, and stakeholder collaboration are crucial for successful implementation. By leveraging AI and IoT, Indonesia can significantly improve waste management efficiency, minimize environmental impact, and advance circular economy initiatives. The findings suggest that, with the right policies and investments, AI-driven waste management can drive sustainability, reduce waste mismanagement, and promote resource optimization, making it a vital strategy for future urban development in Indonesia.
Railway safety research: Mapping trends, strategic clusters, and future pathways Fuqaha, Sameh
Journal of Railway Transportation and Technology Vol. 4 No. 1 (2025): March
Publisher : Politeknik Perkeretaapian Indonesia Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37367/jrtt.v4i1.55

Abstract

This study presents a comprehensive bibliometric analysis of railway safety research from 2019 to 2024, offering critical insights into the thematic evolution, intellectual structure, and future pathways in this vital domain. By analyzing 445 peer-reviewed articles retrieved from leading academic databases, the study identifies major research clusters centered around risk assessment, human factors, and AI-enabled infrastructure monitoring. The findings reveal a significant shift toward intelligent safety systems, with deep learning, predictive maintenance, and human reliability modeling emerging as dominant themes. China, the United Kingdom, and India are identified as leading contributors, with strong international collaboration driving innovation in the field. Despite notable progress, the analysis uncovers persistent gaps—particularly in cybersecurity resilience, cognitive integration in risk assessment, infrastructure adaptation to climate risks, and localization technologies for autonomous train systems. Future research directions are proposed to address these gaps, including multi-sensor fusion for train positioning, AI-based decision-making frameworks for autonomous operations, and integration of human factors into machine learning-based safety evaluations.
Evaluating the Performance of Python-Based Machine Learning in Earthquake-Resistant Building Design: Fuqaha, Sameh; Nugroho , Guntur
Rekayasa Sipil Vol. 19 No. 2 (2025): Rekayasa Sipil Vol. 19 No. 2
Publisher : Department of Civil Engineering, Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.rekayasasipil.2025.019.02.9

Abstract

This study investigates the feasibility of applying artificial intelligence (AI)-based machine learning techniques, specifically a Multiple Linear Regression (MLR) model implemented in Python, for earthquake-resistant building design. The AI-based predictions are compared against conventional SAP2000 structural analysis. As one of the most seismically active regions globally, Indonesia urgently requires efficient and accurate seismic design methodologies. Traditional approaches, while reliable, are often time-consuming and labor-intensive, whereas AI offers rapid data processing and automation. This research predicted key structural parameters—including mass participation ratio, base shear force, inter-story drift, and structural period—using the MLR model and benchmarked against SAP2000 simulations. The AI-based predictions exhibited excellent alignment, with an average deviation of only 0.016%. Statistical validation showed an R² score of 0.999 and a p-value of 0.738, confirming no significant difference between the two methods. Moreover, the AI model significantly reduced computational time, completing analyses within seconds compared to the extended duration required by SAP2000. Despite these advantages, the current AI framework lacks a 3D modeling interface, limiting its applicability for detailed structural design. Future research should enhance AI capabilities by integrating parametric modeling tools and Building Information Modeling (BIM) platforms to support broader implementation in earthquake-resistant structural engineering.
Review of Fly Ash-Based Zero-Cement Concrete Performance Fuqaha, Sameh; Zaki , Ahmad; Nugroho, Guntur
JURNAL SAINTIS Vol. 25 No. 02 (2025)
Publisher : UIR Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/saintis.2025.vol25(02).21840

Abstract

The urgent need to reduce the environmental impact of construction materials has led to increasing interest in sustainable alternatives to Ordinary Portland Cement (OPC). Among emerging solutions, Zero-Cement Concrete (ZCC) utilizing fly ash (FA) as a primary binder offers a viable pathway for lowering CO₂ emissions and reusing industrial by-products. This review investigates the key components, mixing mechanisms, curing conditions, and mechanical performance of FA-based ZCC. FA, particularly Class F and Class C, in combination with alkaline activators such as sodium hydroxide (NaOH) and sodium silicate (Na₂SiO₃), plays a crucial role in the geopolymerization process that forms the cementitious matrix. The compressive strength, modulus of elasticity, and flexural strength of ZCC are influenced by multiple factors, including activator molarity, SS/SH ratio, binder-aggregate proportions, and curing regime. Experimental studies indicate that with optimized mixing and curing parameters, FA–ZCC can achieve mechanical performance comparable to or exceeding OPC concrete. However, the absence of standardized mix design procedures and field-curing strategies remains a challenge. This study highlights the need for further research on durability, life-cycle assessment, and in-situ applications to fully realize the potential of ZCC as a mainstream, eco-efficient construction material.
Seismic Stiffness Evaluation of RC Dual Systems in Varying Geometries: A Pushover-Based Study Using Indonesian Codes Fuqaha, Sameh; Nugroho , Guntur
Jurnal Teknik Sipil dan Perencanaan Vol. 27 No. 2 (2025): Jurnal Teknik Sipil dan Perencanaan
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jtsp.v27i2.25494

Abstract

This study evaluates the seismic performance of reinforced concrete (RC) dual systems that combine moment-resisting frames with shear walls, using nonlinear pushover analysis in accordance with Indonesian seismic design codes (SNI 1726:2019 and SNI 2847:2019). A total of 32 analytical models were developed to examine the influence of four critical parameters: story height (3–10 stories), span length (5.5–6.5 m), shear wall thickness (200–250 mm), and concrete compressive strength (20–25 MPa). The elastic stiffness factor was determined as the base shear ratio to roof displacement at the onset of first hinge formation. In contrast, base shear capacity was derived from the pushover curves. Results show that geometric parameters exert the most decisive influence on seismic response, with stiffness decreasing by more than 50 percent as story height increases and by approximately 8 percent with longer spans. Material enhancements provide only modest gains of 2 to 7 percent. These findings emphasize the dominant role of structural configuration in drift control and ductility demand, offering practical recommendations for optimizing RC dual systems under Indonesian codes and improving the resilience of mid- to high-rise buildings in seismic regions.
Co-Authors Nugroho , Guntur Nugroho, Guntur Nursetiawan Nursetiawan, Nursetiawan Zaki , Ahmad