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All Journal Journal of Technology Informatics and Engineering Civil Engineering Science and Technology (CEST)
Ibrahim, Said Maulana
Unknown Affiliation
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Mechanical Engineering

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Computational Fluid Dynamics (CFD) Optimization in Smart Factories: AI-Based Predictive Modelling Ibrahim, Said Maulana; Najmi, M. Ikhwan
Journal of Technology Informatics and Engineering Vol. 4 No. 1 (2025): APRIL | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i1.264

Abstract

In the era of Industry 4.0, optimizing fluid flow systems in smart factories is essential to improve energy efficiency and operational stability. Traditional Computational Fluid Dynamics (CFD) simulations provide accurate fluid flow analysis but require extensive computational resources and long processing times, making real-time applications challenging. To address this limitation, this study aims to develop an AI-based predictive model for CFD simulations, utilizing Convolutional Neural Networks (CNN) and Extreme Gradient Boosting (XGBoost) to accelerate the estimation of fluid flow characteristics in industrial environments. The research methodology involves generating CFD simulation datasets, preprocessing data, and training AI models to predict key fluid parameters such as pressure, velocity, and temperature. The evaluation results show that CNN achieves a Mean Squared Error (MSE) of 0.0025 and a Root Mean Squared Error (RMSE) of 0.05, outperforming XGBoost, which records an MSE of 0.0030 and an RMSE of 0.055. Moreover, CNN predicts fluid dynamics in just 15.2 seconds, while XGBoost achieves results in 10.5 seconds, compared to the 1200.5 seconds required by traditional CFD simulations. These findings highlight the potential of AI in reducing computation time by over 98%, making real-time fluid flow analysis feasible in industrial settings. This study contributes to the advancement of AI-integrated CFD modeling, demonstrating that AI can significantly enhance the efficiency of fluid dynamics analysis without compromising accuracy. Future research should focus on expanding AI models to handle more complex flow conditions and integrating AI with smart factory design tools for real-time optimization
Scalable and Secure IoT-Driven Vibration Monitoring: Advancing Predictive Maintenance in Industrial Systems Ibrahim, Said Maulana; Go, Eun-Myeong; Iranda, Jennifer
Journal of Technology Informatics and Engineering Vol. 3 No. 3 (2024): December (Special Issue: Big Data Analytics) | JTIE: Journal of Technology Info
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i3.210

Abstract

The rapid evolution of Industry 4.0 has positioned Internet of Things (IoT) technologies as key enablers for smarter industrial operations, particularly in predictive maintenance and machine monitoring. This research proposes an innovative IoT-driven vibration monitoring system that addresses limitations in traditional approaches such as high costs, limited scalability, and insufficient real-time capabilities. Employing low-cost sensors, edge computing, and LoRaWAN-based communication, the framework enables efficient fault detection and operational analysis. Data from industrial machinery was collected over two months and analyzed using advanced signal processing and machine learning techniques to extract meaningful insights. The system demonstrated an accuracy rate of 92%, a detection latency of 150 milliseconds, and extended sensor life to 12 months, marking significant improvements over conventional methods. Furthermore, scalability tests showed stable performance across setups involving up to 500 sensors, even in challenging industrial conditions. This study also highlights cost reductions of 30% and a 25% decline in machine downtime, reinforcing its practical value for industrial applications. By delivering an adaptable, energy-efficient, and secure solution, this research advances the integration of IoT into industrial systems. It lays the groundwork for future enhancements, including real-world testing and multimodal data integration
Performance Evaluation of Recycled Concrete Aggregates in Seismic-Resistant Structural Design Ibrahim, Said Maulana; Prasyas, Anel
Civil Engineering Science and Technology Vol. 1 No. 1 (2025): March | CEST (Civil Engineering Science and Technology)
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/9d6ba216

Abstract

The increasing demand for construction materials has led to excessive exploitation of natural aggregates, raising concerns about environmental sustainability and carbon emissions. Recycled Concrete Aggregate (RCA) has been introduced as a potential alternative to natural aggregates, but its application in seismic-resistant structures remains a challenge due to its lower mechanical properties. This study aims to evaluate the structural performance of RCA-based concrete in seismic applications by analyzing its compressive strength, tensile strength, elastic modulus, and cyclic loading resistance. An experimental approach was employed, where concrete samples with RCA proportions of 0%, 25%, 50%, 75%, and 100% were tested under standardized laboratory conditions. The results indicate that RCA can be used up to 50% without significant loss of compressive strength, which remained above 30 MPa. However, at RCA proportions above 50%, compressive strength decreased by up to 30%, and the elastic modulus dropped from 30.2 GPa (0% RCA) to 20.8 GPa (100% RCA). Cyclic loading tests further revealed a reduction in energy dissipation capacity, from 85 kJ at 0% RCA to 55 kJ at 100% RCA, and an increase in residual deformation. These findings highlight the need for mix optimization in high-RCA concrete, such as incorporating supplementary materials like fly ash, nano-silica, or fiber reinforcement to enhance mechanical performance. This study contributes to the sustainable development of construction materials by providing insights into the feasibility and limitations of RCA in seismic-resistant structures
Co-Authors Go, Eun-Myeong Iranda, Jennifer Najmi, M. Ikhwan Prasyas, Anel