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All Journal International Transactions on Artificial Intelligence (ITALIC) Journal of Computer Science and Technology Application
Sipah Audiah
CAI Sejahtera Indonesia
Computer Science & IT Control & Systems Engineering Library & Information Science Other

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Transforming Energy and Resource Management with AI: From Theory to Sustainable Practice Zaharuddin; Sipah Audiah; Yulia Putri Ayu Sanjaya; Ora Pertiwi Daeli; Michael Johnson
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.v2i2.554

Abstract

Efficient and sustainable energy management is crucial for addressing global environmental challenges. Artificial intelligence (AI) has emerged as a significant tool in the energy revolution, enhancing operational efficiency and integrating renewable energy sources. This study examines the impact of AI on optimizing energy and resource management, focusing on increasing renewable energy use and efficiency. Using a quantitative and exploratory approach, data from 100 energy companies that have implemented AI solutions were analyzed. The findings show that AI can improve energy efficiency by 25%, strengthen sustainable operations, and reduce environmental impact. These results align with Complex Systems Theory, highlighting that advanced technologies like AI enhance system adaptability and efficiency. Despite these insights, the study is limited to companies that have adopted AI and focuses solely on the energy sector. This highlights the need for broader research across various sectors and geographic contexts. The implications suggest that AI not only improves energy management but also supports global sustainability efforts, making it vital for a sustainable energy future.
Advanced Cyber Threat Detection: Big Data-Driven AI Solutions in Complex Networks Agung Rizky; Muhammad Zaki Firli; Nur Aulia Lindzani; Sipah Audiah; Lukita Pasha
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.v1i2.42

Abstract

In the rapidly evolving digital landscape, cybersecurity has become increasingly critical, especially within complex network environments. This research presents the development of a cyber threat detection system that leverages Artificial Intelligence (AI) and Big Data analytics to enhance accuracy and speed in identifying and responding to cyber threats. The system was evaluated through rigorous testing, demonstrating a high detection accuracy of 95\% for malware and unauthorized access attempts, along with an impressive detection speed of 2 seconds on average for most threats. Additionally, the system exhibited strong scalability, maintaining optimal performance even with increasing network complexity. These findings underscore the system's robustness and practical applicability in real-world scenarios. However, further refinement is suggested to improve anomaly detection and reduce response times for more complex threats. This study contributes valuable insights into the integration of AI and Big Data in cybersecurity, providing a scalable and effective solution for protecting critical network infrastructures.
Co-Authors Agung Rizky Lukita Pasha Michael Johnson Muhammad Zaki Firli Nur Aulia Lindzani Ora Pertiwi Daeli Yulia Putri Ayu Sanjaya Zaharuddin