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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 56 Documents
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Innovation in Sustainable Construction Materials in Green Infrastructure Development Mahendra, I Made Agus
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1556

Abstract

The construction industry significantly impacts environmental sustainability, prompting the need for innovative materials that minimize ecological footprints. Sustainable construction materials play a crucial role in the development of green infrastructure, aimed at enhancing urban resilience and promoting environmental conservation. This study aims to explore various innovative sustainable construction materials and their applications in green infrastructure projects. It seeks to identify the benefits and challenges associated with these materials in promoting eco-friendly building practices. A comprehensive literature review was conducted, analyzing recent advancements in sustainable construction materials, including recycled materials, bio-based composites, and smart materials. Case studies of successful green infrastructure projects utilizing these materials were examined to assess their effectiveness and sustainability. The findings reveal that innovative materials such as recycled concrete, bamboo, and mycelium composites significantly reduce carbon emissions and resource consumption. Case studies demonstrated improved energy efficiency and reduced waste in projects that employed these materials. Challenges related to cost, availability, and regulatory standards were also identified. The research concludes that the integration of innovative sustainable materials is vital for the advancement of green infrastructure. Emphasizing the benefits of these materials can lead to broader adoption in the construction industry. Future research should focus on overcoming the identified challenges and developing standardized guidelines to facilitate the use of sustainable materials in infrastructure projects
Case Study of the Use of Recycled Concrete in Highway Projects Ilham, Ilham; Ngii, Edward; One, La
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1558

Abstract

The increasing demand for sustainable construction practices has led to the exploration of recycled materials, particularly in road projects. Recycled concrete not only reduces waste but also offers potential environmental and economic benefits. Understanding its effectiveness in real-world applications is essential for promoting its wider use. This study aims to evaluate the performance of recycled concrete in highway construction projects, assessing its structural integrity, cost-effectiveness, and environmental impact. It seeks to provide insights into the feasibility of using recycled materials in road infrastructure. A case study approach was employed, analyzing three highway projects that utilized recycled concrete. Data were collected through site inspections, material testing, and interviews with project managers. Key performance indicators, such as compressive strength, durability, and cost savings, were evaluated. The findings indicate that recycled concrete performed comparably to traditional concrete in terms of compressive strength and durability. Cost analysis revealed significant savings, with recycled concrete reducing material costs by an average of 15%. Environmental assessments highlighted notable reductions in carbon emissions and landfill waste. The research concludes that recycled concrete is a viable option for highway construction, offering both economic and environmental advantages. The positive outcomes from the case studies support the broader adoption of recycled materials in infrastructure projects. Future studies should focus on long-term performance and the development of guidelines for integrating recycled concrete in various construction applications.
Suspension Bridge Simulation Modeling in Overcoming Seismic Loads Krit, Pong; Som, Rit; Chai, Som
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1559

Abstract

The increasing frequency of seismic events poses significant challenges to the structural integrity of suspension bridges. Understanding how these structures respond to seismic loads is essential for ensuring their safety and performance. Effective modeling and simulation techniques can provide valuable insights into the behavior of suspension bridges under such conditions. This study aims to develop a simulation model for suspension bridges to assess their performance under seismic loading. The research seeks to identify critical factors influencing the bridge's response and propose design modifications to enhance resilience against earthquakes. A finite element analysis (FEA) approach was employed to create a detailed simulation model of a suspension bridge. Various seismic scenarios were simulated using different ground motion records. Key parameters, including displacement, stress, and dynamic response, were monitored and analyzed to evaluate the bridge's behavior. The simulation results indicated that the suspension bridge exhibited significant displacement under seismic loads, particularly at the midspan. Stress concentrations were observed at critical joints and cables, highlighting potential failure points. Design modifications, such as increased cable tension and enhanced damping systems, were proposed to improve the bridge's seismic performance. The research concludes that simulation modeling is a valuable tool for understanding the seismic response of suspension bridges. The findings emphasize the importance of incorporating seismic considerations into the design process. Future research should focus on validating the simulation results with experimental data and exploring advanced materials and technologies to further enhance bridge resilience.
Development of Machine Learning Algorithms for Anomaly Detection in Internet of Things (IoT) Networks Rith, Vicheka; Sok, Vann; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1560

Abstract

The proliferation of Internet of Things (IoT) devices has increased the vulnerability of networks to security threats, making anomaly detection essential for maintaining system integrity. Traditional security measures often fall short in identifying and mitigating complex attack patterns that can jeopardize IoT networks. This research aims to develop a machine learning algorithm specifically designed for anomaly detection in IoT environments. The goal is to enhance the ability to identify unusual behavior indicative of potential security breaches while minimizing false positives. A dataset comprising network traffic from various IoT devices was collected and preprocessed to extract relevant features. Several machine learning algorithms, including decision trees, support vector machines, and neural networks, were implemented and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were used to assess the effectiveness of each model. The results indicated that the proposed machine learning algorithm outperformed traditional methods, achieving an accuracy of 95% in detecting anomalies. The model demonstrated a significant reduction in false positives compared to existing techniques, thereby enhancing the reliability of anomaly detection in IoT networks. The research concludes that the developed machine learning algorithm is a robust solution for detecting anomalies in IoT environments. This advancement contributes to the field by providing an effective tool for improving security measures in the rapidly evolving landscape of IoT. Future work should focus on real-time implementation and further optimization of the algorithm to adapt to dynamic network conditions.
Effectiveness of Deep Learning Models in Cybercrime Prediction Mustofa, Muhammad; Akhtar, Shazia; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i5.1561

Abstract

The rise of cybercrime poses significant challenges to security agencies and organizations worldwide. Traditional methods of crime prediction often fall short in accurately identifying potential threats. As a result, there is a growing interest in leveraging advanced technologies, such as deep learning, to enhance predictive capabilities in cybersecurity. This research aims to evaluate the effectiveness of deep learning models in predicting cybercrime incidents. The study investigates how these models can improve accuracy and reliability compared to conventional prediction techniques. A dataset comprising historical cybercrime incidents was collected and preprocessed to extract relevant features. Various deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), were implemented. The models were trained and validated using a portion of the data, while performance metrics such as accuracy, precision, recall, and F1-score were used to assess their predictive capabilities. The findings indicate that deep learning models significantly outperform traditional methods in predicting cybercrime incidents. The best-performing model achieved an accuracy of 92%, showcasing its ability to identify complex patterns in the data. Additionally, deep learning models demonstrated lower false positive rates, enhancing their reliability in real-world applications. The research concludes that deep learning is a powerful tool for predicting cybercrime, offering enhanced accuracy and efficiency. These findings contribute to the field by highlighting the potential of advanced machine learning techniques in improving cybersecurity measures. Future work should focus on refining these models and exploring their applicability in real-time cyber threat detection.
Performance Analysis of Cloud Computing Systems in Collaborative Software Development Environments Li, Zhang; Xiang, Yang; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1562

Abstract

The rise of cloud computing has transformed software development, enabling collaborative environments that enhance productivity and efficiency. However, the performance of cloud computing systems in supporting collaborative software development remains an area of active research, with various factors influencing effectiveness. This study aims to analyze the performance of cloud computing systems in collaborative software development environments. The focus is on identifying key performance metrics and their impact on team productivity and project outcomes. A mixed-methods approach was employed, combining quantitative performance metrics and qualitative surveys from development teams using cloud-based tools. Key metrics analyzed included system uptime, response time, and resource utilization. Surveys gathered insights on user satisfaction and perceived efficiency improvements. The findings reveal that cloud computing systems significantly enhance collaboration among software development teams. Metrics indicated an average system uptime of 99.5%, with response times averaging under 200 milliseconds. Survey results showed that 85% of participants reported increased productivity when using cloud-based tools compared to traditional methods. The research concludes that cloud computing systems provide substantial performance advantages in collaborative software development environments. These systems facilitate better communication, resource sharing, and project management, ultimately leading to improved project outcomes. Future research should explore the long-term effects of cloud computing on software development practices and its implications for team dynamics.
Blockchain Based Software Development for Digital Identity Management Systems Tan, Ethan; Linm, Sofia; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1563

Abstract

The increasing prevalence of digital identities has raised concerns about security, privacy, and data ownership. Traditional identity management systems often lack transparency and are vulnerable to breaches, necessitating more secure alternatives. Blockchain technology offers a decentralized approach that can enhance the security and integrity of digital identity management. This research aims to develop a blockchain-based software solution for digital identity management systems. The study focuses on creating a secure, user-centric platform that allows individuals to control their personal information while ensuring data integrity and privacy. A design-based research approach was employed, involving the development of a prototype using Ethereum blockchain technology. The system architecture was designed to facilitate secure identity verification and data storage. User testing was conducted to evaluate usability and effectiveness, with feedback collected through surveys and interviews. The prototype demonstrated significant improvements in security and user control over personal data. Key features included decentralized storage of identity information, smart contracts for verification processes, and enhanced privacy measures. User feedback indicated a high level of satisfaction with the system's usability and perceived security. The research concludes that blockchain technology presents a viable solution for digital identity management, offering enhanced security and user control. The developed software prototype demonstrates the potential for broader applications in various sectors, paving the way for future research to explore scalability and integration with existing identity management frameworks.
Parallel Processing System Optimization in High-Performance Computing for Fluid Simulation Yamamoto, Sota; Tanaka, kaito; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1565

Abstract

The growing complexity of fluid simulations in computational science necessitates the use of high-performance computing (HPC) systems. Efficient processing is critical for handling large datasets and complex algorithms, particularly in fields such as aerospace, meteorology, and biomedical engineering. Existing parallel processing methods often face limitations in scalability and resource utilization. This research aims to optimize parallel processing systems for high-performance computing applications in fluid simulations. The study focuses on enhancing computational efficiency and reducing execution time while maintaining accuracy in simulations. A multi-faceted approach was employed, combining algorithmic improvements with architectural enhancements. The research involved implementing advanced parallelization techniques, such as domain decomposition and load balancing, on a cluster of HPC nodes. Performance metrics were collected to evaluate the impact of these optimizations on simulation speed and resource utilization. The optimized system demonstrated a significant reduction in execution time, achieving up to a 60% improvement compared to baseline performance. Enhanced load balancing techniques resulted in more efficient resource distribution, leading to improved overall system performance. Accuracy of the fluid simulations remained consistent with previous results, validating the effectiveness of the optimizations. The study concludes that optimizing parallel processing systems significantly enhances the efficiency of fluid simulations in HPC environments. The findings provide valuable insights for researchers and practitioners seeking to improve computational performance in complex simulations. Future work should explore further optimizations and the integration of emerging technologies to continue advancing the capabilities of fluid simulation in high-performance computing
Application of Model Predictive Control (MPC) in Industrial Automation Robotic Systems Aslam, Bilal; Tariq, Usman; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1566

Abstract

The industrial automation sector is rapidly evolving, with a growing need for advanced control strategies to enhance the efficiency and precision of robotic systems. Model Predictive Control (MPC) has emerged as a promising approach due to its ability to handle multivariable control problems and constraints effectively. However, its application in robotic automation remains underexplored. This research aims to implement Model Predictive Control in industrial robotic systems to improve performance, adaptability, and operational efficiency. The study focuses on evaluating the effectiveness of MPC in real-time robotic applications, specifically in tasks requiring high precision and dynamic response. A simulation-based approach was employed, using a robotic arm model as a testbed for implementing MPC. The control algorithm was designed to predict future states of the system based on current measurements and optimize control inputs accordingly. Performance metrics, including tracking error and response time, were evaluated under various operational scenarios. The implementation of MPC resulted in a significant reduction in tracking error and improved response times compared to traditional control methods. The robotic arm demonstrated enhanced adaptability to changes in the environment and task requirements, showcasing the robustness of the MPC approach. The findings indicate that Model Predictive Control is an effective strategy for enhancing the performance of robotic systems in industrial automation. The successful application of MPC not only improves operational efficiency but also provides a framework for future research into more complex robotic applications. This study contributes to the growing body of knowledge on advanced control methods in automation.  
The Role of Geospatial Engineering in Handling Natural Disasters and Humanitarian Crises Htwe, Thandar; Zaw, Soe Thu; Vandika, Arnes Yuli
Journal of Moeslim Research Technik Vol. 1 No. 6 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/technik.v1i6.1567

Abstract

The background of this research is the increasing frequency and intensity of natural disasters and humanitarian crises that require rapid and effective handling. Geospatial techniques have emerged as an important tool in disaster management, offering solutions for real-time mapping, monitoring, and analysis of emergency situations. The purpose of this research is to evaluate the role and effectiveness of geospatial techniques in handling natural disasters and humanitarian crises, and to identify areas that need improvement. The research method used involves analysis of current literature and case studies of various natural disaster incidents and humanitarian crises around the world. Data is collected from reliable sources such as scientific journals, government reports, and non-governmental organizations. This approach allows researchers to evaluate the practical application of geospatial techniques and identify key factors that influence their success. The results of the study show that geospatial techniques play a vital role in various stages of disaster management, from mitigation, preparedness, response, to recovery. Risk mapping, environmental change monitoring, and spatial analysis have been shown to improve the efficiency and effectiveness of emergency response operations. However, the study also identified challenges such as limited data access, the need for specialized training, and adequate technological infrastructure.The study’s conclusion confirms that geospatial techniques are a crucial component in managing natural disasters and humanitarian crises. Proper implementation can save lives and significantly reduce negative impacts. Therefore, investment in geospatial technologies, human resource training, and infrastructure development should be a priority to improve emergency response capacity in the future.

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