Garuda - Garba Rujukan Digital

AI-Based Strategies to Improve Resource Efficiency in Urban Infrastructure Ninda Lutfiani; Nuke Puji Lestari Santoso; Ridhuan Ahsanitaqwim; Untung Rahardja; Achani Rahmania Az Zahra
International Transactions on Artificial Intelligence Vol. 2 No. 2 (2024): International Transactions on Artificial Intelligence
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/italic.v2i2.545

Rapid urbanization has significantly increased urban populations, leading to higher consumption of resources such as energy, water, and fuel. Resource efficiency is crucial to managing urban growth in an environmentally friendly and economical manner. This research aims to explore the role of artificial intelligence (AI) in improving resource efficiency in urban infrastructure. By leveraging AI technology, this study seeks to find innovative solutions that can optimize resource use, enhance energy management, and improve monitoring and control of infrastructure systems. The findings indicate that the implementation of AI can increase energy efficiency by 15%, reduce transportation travel times by 15%, and improve water management efficiency by 15%. These results demonstrate that AI can be an effective tool in supporting the sustainability of urban infrastructure, reducing operational costs, and mitigating environmental impacts. This research provides practical guidance for city managers and policymakers in designing and implementing smarter and more efficient technological solutions.

Artificial Intelligence in Autonomous Vehicles: Current Innovations and Future Trends Nuraini Diah Noviati; Fengki Eka Putra; Sadan Sadan; Ridhuan Ahsanitaqwim; Nanda Septiani; Nuke Puji Lestari Santoso
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.161

Artificial Intelligence (AI) has become a cornerstone in advancing autonomous vehicles, enabling realtime decision making, object detection, and automation in driving systems. This study aims to explore key AI innovations, including Machine Learning (ML) algorithms, computer vision, and reinforcement learning, that contribute to the development of autonomous vehicles. A qualitative approach} was adopted to analyze both current applications and future innovations of AI in autonomous vehicles. The study highlights various current AI applications in autonomous vehicles, such as automated safety features, advanced navigation systems, and adaptive cruise control. These technologies demonstrate how AI enhances vehicle functionality and improves safety in today driving environment. Looking ahead, AI is expected to enable full autonomy in vehicles, foster integration with smart city infrastructures, and drive innovations in fleet management. These advancements are anticipated to significantly improve vehicle safety, operational efficiency, and the overall user experience, solidifying AI as the fundamental technology for the future of intelligent transportation systems.

Artificial Intelligence in Autonomous Vehicles: Current Innovations and Future Trends Nuraini Diah Noviati; Fengki Eka Putra; Sadan Sadan; Ridhuan Ahsanitaqwim; Nanda Septiani; Nuke Puji Lestari Santoso
International Journal of Cyber and IT Service Management (IJCITSM) Vol. 4 No. 2 (2024): October
Publisher : International Institute for Advanced Science & Technology (IIAST)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ijcitsm.v4i2.161

Artificial Intelligence (AI) has become a cornerstone in advancing autonomous vehicles, enabling realtime decision making, object detection, and automation in driving systems. This study aims to explore key AI innovations, including Machine Learning (ML) algorithms, computer vision, and reinforcement learning, that contribute to the development of autonomous vehicles. A qualitative approach} was adopted to analyze both current applications and future innovations of AI in autonomous vehicles. The study highlights various current AI applications in autonomous vehicles, such as automated safety features, advanced navigation systems, and adaptive cruise control. These technologies demonstrate how AI enhances vehicle functionality and improves safety in today driving environment. Looking ahead, AI is expected to enable full autonomy in vehicles, foster integration with smart city infrastructures, and drive innovations in fleet management. These advancements are anticipated to significantly improve vehicle safety, operational efficiency, and the overall user experience, solidifying AI as the fundamental technology for the future of intelligent transportation systems.

Advancements and Challenges in the Implementation of 5G Networks: A Comprehensive Analysis Mahyuni; Ade Arya Bimantara; Rifky Nurfaizi; Ridhuan Ahsanitaqwim; Victorianda
CORISINTA Vol 1 No 2 (2024): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v1i2.32

The evolution of cellular networks from 1G to 5G has introduced significant advancements in speed, capacity, and reliability. Now, 5G is set to transform communication technology further with higher speeds, increased capacity, reduced latency, and massive IoT connectivity. This research aims to identify the opportunities and challenges in the implementation of 5G networks, focusing on improvements in network speed and capacity, IoT development, industrial applications, user experience, and infrastructure, security, privacy, regulatory, and spectrum challenges. A mixed-methods approach was used, combining qualitative and quantitative analyses. Data were collected from primary sources (expert interviews, surveys) and secondary sources (academic literature, industry reports). Thematic analysis and descriptive and inferential statistics were applied. 5G significantly enhances network speed and capacity, enabling faster, more reliable communication and greater device connectivity. It supports industrial automation, operational efficiency, and innovation in sectors like healthcare, automotive, and manufacturing. Despite its potential, 5G faces challenges such as high infrastructure costs, coverage issues, and security risks. Effective collaboration between government and industry, prioritizing advanced technologies, and developing a comprehensive 5G ecosystem are essential for successful implementation.

Artificial Intelligence in Predictive Cybersecurity: Developing Adaptive Algorithms to Combat Emerging Threats Sudaryono Sudaryono; Rusdi Pratomo; Ahmad Ramadan; Ridhuan Ahsanitaqwim; Eamon Fletcher
CORISINTA Vol 2 No 1 (2025): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i1.55

The exponential growth of digital systems has introduced significant cybersecurity challenges, exposing vulnerabilities to increasingly sophisticated threats. Traditional security measures, which rely on static and signature-based methods, often fail to adapt to the dynamic nature of cyberattacks, highlighting the need for innovative solutions. This study aims to develop and evaluate adaptive algorithms in predictive cybersecurity, leveraging Artificial Intelligence (AI) to combat emerging threats such as zero-day exploits and advanced persistent threats (APTs). A simulation-based research design was employed, integrating reinforcement learning frameworks like Deep Q-Learning and utilizing datasets such as CICIDS2017 and synthetic data for zero-day threat simulations. The results show that adaptive algorithms achieved 94.8% detection accuracy, reduced false positives by 54.5%, and improved response times by 53.1%, significantly outper forming static models. Additionally, the adaptive systems demonstrated superiorcapacity to identify novel threats in simulated attack scenarios. These findings underscore the potential of adaptive AI algorithms to revolutionize predictive cybersecurity by offering dynamic, real-time responses to evolving threats. Despite their computational demands posing challenges for smaller organizations, integrating techniques such as adversarial training and robust anomaly detection can enhance resilience. That adaptive algorithms can enhance the resilience and reliability of cybersecurity systems, advocating for future integration with technologies like blockchain and edge computing to address scalability and latency issues. These advancements pave the way for more robust and proactive cybersecurity defenses in an increasingly interconnected digital landscape.

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