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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 5 Documents
 1 
Search results for , issue "Vol. 1 No. 5 (2024)" : 5 Documents clear
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.

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