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Rijois Iboy Erwin Saragih
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Jl. Karya Bakti Gg. Dame No. 95, kelurahan Indra Kasih, Kecamatan Medan Tembung, Sumatera Utara
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International Journal of Information System and Innovative Technology
Published by Geviva Edukasi Trans Teknologi
ISSN : -     EISSN : 29647207     DOI : https://doi.org/10.63322/ijisit
Core Subject : Science,
Computer Science & IT
IJISIT (International Journal of Information System and Innovative Technology) is a peer-reviewed journal in Applied Information Technology published twice a year in June and December and organized by the PT Geviva Edukasi Trans Teknologi. Focus & Scope International Journal of Information System & Innovative Technology aims to publish original research results on the implementation of the information systems. International Journal of Information System & Innovative Technology covers a broad range of research topics in information technology. The topics include but are not limited to avionics. 1. Artificial Intelligence and Soft Computing 2. Computer Science and Information Technology 3. Telecommunication System and Security 4. Digital Signal, Image and Video Processing 5. Automation, Instrumentation and Control Engineering 6. Internet of Things, Big Data and Cloud Computing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 2 (2024): December" : 5 Documents clear
AI-Powered Education: Transforming Learning through Personalized and Scalable Solutions Saragih, Rijois Iboy Erwin
International Journal of Information System and Innovative Technology Vol. 3 No. 2 (2024): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/qm9dk118

Abstract

The rapid evolution of Artificial Intelligence (AI) has profoundly influenced various sectors, with education emerging as a pivotal area of transformation. The integration of AI into educational systems is redefining teaching methodologies, learning experiences, and administrative efficiencies. However, this intersection of AI and education faces significant challenges, including disparities in access, ethical concerns, and the lack of standardized frameworks for implementation. To address these challenges, this paper proposes a comprehensive AI-powered educational framework designed to personalize learning experiences and scale educational delivery efficiently. The framework incorporates a multi-layered architecture consisting of intelligent tutoring systems, adaptive learning platforms, and automated assessment tools. These components are designed to leverage AI algorithms such as natural language processing, predictive analytics, and machine learning to analyze student data, identify learning gaps, and deliver customized content. The proposed solution was evaluated through case studies and pilot implementations, demonstrating improved learner engagement, enhanced knowledge retention, and optimized resource utilization. Key findings include a 25% improvement in learning outcomes in personalized environments and increased teacher productivity by automating repetitive tasks. This research contributes to the field by offering a scalable and practical model for integrating AI into educational systems. It highlights ethical considerations, emphasizes the importance of inclusivity, and underscores the need for interdisciplinary collaboration. Finally, the paper presents actionable recommendations, including policy guidelines, strategies for addressing equity challenges, and a roadmap for future research. These recommendations aim to guide educators, technologists, and policymakers in harnessing the full potential of AI to create more equitable and effective learning ecosystems.
Enhancing Cyber-Physical System Security: A Review of Detection and Mitigation Techniques Simanjuntak, Thandy
International Journal of Information System and Innovative Technology Vol. 3 No. 2 (2024): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/zny3qg45

Abstract

The increasing sophistication of cyber threats has driven the demand for a well-trained cybersecurity workforce. Cyber Ranges have emerged as essential platforms for hands-on training, skill development, and cybersecurity research. This paper presents a comprehensive review of Cyber Ranges, focusing on their role in cybersecurity workforce development across different regions and industries. We examine global practices, existing frameworks, and standardization efforts that shape the implementation and effectiveness of Cyber Ranges. Furthermore, we analyze the challenges in designing scalable, realistic, and adaptive training environments, considering advancements in artificial intelligence (AI), gamification, and cloud-based simulations. By evaluating best practices and identifying gaps in current methodologies, this review provides actionable insights for policymakers, educators, and industry stakeholders. The findings underscore the necessity of harmonizing Cyber Range capabilities with real-world cybersecurity demands, ensuring a resilient and highly skilled workforce ready to combat emerging threats.
Cybersecurity Challenges and AI-Powered Mitigation Strategies in CCTV Surveillance Systems Sutanto, Yulius
International Journal of Information System and Innovative Technology Vol. 3 No. 2 (2024): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/fsk65573

Abstract

CCTV surveillance systems are essential for security and crime prevention but are increasingly vulnerable to cyber threats such as unauthorized access, data breaches, and deep-fake manipulations. Traditional security measures often fail against sophisticated attacks, necessitating advanced protection mechanisms. This study explores cybersecurity challenges in CCTV networks and proposes an intelligent mitigation framework to enhance security. The research analyses existing vulnerabilities, including malware attacks and data interception, highlighting gaps in current security measures. To address these threats, we introduce a machine learning-based intrusion detection system (IDS) for real-time anomaly detection. Additionally, blockchain technology is integrated to secure CCTV footage integrity, preventing unauthorized alterations.The proposed approach is tested on real-world datasets, evaluating detection accuracy, false positives, and resilience against cyberattacks. Our findings contribute to intelligent cybersecurity solutions for CCTV systems, offering law enforcement and organizations a robust framework for securing surveillance infrastructures.
A Review of AI-Driven Predictive Maintenance in Telecommunications Silitonga, Joe Laksamana
International Journal of Information System and Innovative Technology Vol. 3 No. 2 (2024): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/tsq25y55

Abstract

The telecommunications industry is rapidly evolving, driven by the increasing reliance on artificial intelligence (AI) to enhance network reliability and efficiency. Predictive maintenance (PdM) powered by AI has emerged as a crucial strategy for minimizing unexpected downtimes and optimizing service quality. Traditional reactive maintenance approaches often lead to inefficiencies, operational delays, and increased costs. This paper provides a comprehensive review of AI-driven predictive maintenance in telecommunications, categorizing existing research based on AI methodologies, applications, and real-world implementations. We analyze machine learning (ML), deep learning (DL), and explainable AI (XAI) techniques in fault detection, resource allocation, and performance optimization. A comparative analysis highlights the advantages and challenges of AI adoption, emphasizing key research gaps in scalability, ethical considerations, and integration with emerging technologies such as 5G, edge computing, and the Internet of Things (IoT). This study concludes by outlining future research directions and advocating for responsible AI deployment to ensure transparency, trust, and long-term sustainability in AI-driven predictive maintenance.
AI in Accounting and Finance: A Literature Review on Challenges, Opportunities, and Ethical Considerations Simatupang, Oktaria
International Journal of Information System and Innovative Technology Vol. 3 No. 2 (2024): December
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/t6g9n640

Abstract

The integration of Artificial Intelligence (AI) in accounting and finance has significantly transformed traditional practices by enhancing efficiency, accuracy, and decision-making. This paper presents a structured literature review exploring the opportunities AI provides, including automation, data analysis, and fraud detection, while also discussing challenges such as transparency, data security, and ethical concerns. A comparative analysis of existing research highlights the key differences in AI adoption across industries and organizations. The study also identifies research gaps, particularly in ethical AI implementation, workforce transformation, and AI adoption among small and medium-sized enterprises (SMEs). By addressing these gaps, the paper contributes to a better understanding of how AI can be responsibly integrated into accounting and

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