cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rijois Iboy Erwin Saragih
Contact Email
rijoissaragih@gmail.com
Phone
+6282163892782
Journal Mail Official
rijoissaragih@gmail.com
Editorial Address
Jl. Karya Bakti Gg. Dame No. 95, kelurahan Indra Kasih, Kecamatan Medan Tembung, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
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 35 Documents
 1   2   3   4 
BKD Files Management Application Saragih, Rijois I. E.
International Journal of Information System and Innovative Technology Vol. 3 No. 1 (2024): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/4atf8j34

Abstract

The Lecturer BKD Files Management Application is a system designed to facilitate digital management of Lecturer Tri Dharma (BKD) files. This application aims to simplify the process of recording, archiving and reporting BKD which is often complicated and time consuming if done manually. With features such as file upload, document search, and archive management, this application allows lecturers to easily manage and track various documents related to their BKD. Apart from that, this application also makes it easy for the administration to verify and monitor lecturers' BKD efficiently. The implementation of this system is expected to increase effectiveness and accuracy in BKD management, as well as reduce the administrative burden faced by lecturers and the administration. This research contributes to the academic community by providing a practical tool that enhances the efficiency of BKD management. The application reduces administrative overhead, minimizes errors associated with manual processes, and improves the transparency and accessibility of BKD records. Furthermore, this study offers insights into the challenges and best practices in developing digital solutions for academic administrative tasks, potentially serving as a model for similar initiatives in other educational institutions
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
Evaluating the Effectiveness and Ethical Considerations of CCTV Surveillance in Public Spaces: A Cybersecurity Perspective Sutanto, Yulius
International Journal of Information System and Innovative Technology Vol. 3 No. 1 (2024): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/2kfhm657

Abstract

CCTV surveillance plays a crucial role in modern security systems, widely implemented in public spaces, transportation hubs, and commercial settings to enhance safety and deter crime. Technological advancements, including artificial intelligence, facial recognition, and real-time analytics, have expanded its capabilities, positioning CCTV as both a preventive and investigative tool. However, debates persist regarding its actual impact on crime reduction, with some studies indicating that CCTV lowers criminal activity in well-monitored locations, while others argue that it merely displaces crime to unmonitored areas. Additionally, concerns over cybersecurity risks, data privacy, and unauthorized access to surveillance networks present significant challenges that threaten public trust and system reliability. The global expansion of CCTV further raises ethical and legal dilemmas related to mass surveillance, government overreach, and individual privacy rights. This study critically examines the effectiveness of CCTV in crime prevention, its role in security preparedness, and the cybersecurity vulnerabilities associated with its implementation. By reviewing key research findings, it offers policy recommendations to strengthen CCTV deployment through enhanced cybersecuritymeasures, privacy protections, and ethical oversight. These insights aim to assist policymakers, law enforcement agencies, and cybersecurity professionals in optimizing CCTV applications while mitigating the risks of surveillance misuse.
Emerging Cybersecurity Threats in the Era of AI and IoT: A Risk Assessment Framework Using Machine Learning for Proactive Threat Mitigation Simanjuntak, Thandy
International Journal of Information System and Innovative Technology Vol. 3 No. 1 (2024): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/y3bfp253

Abstract

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has revolutionized various industries, enabling automation, real-time decision-making, and enhanced connectivity. However, these advancements have also introduced new cybersecurity threats, increasing the vulnerability of interconnected systems. The proliferation of IoT devices and AI-driven applications has expanded the attack surface, making them prime targets for cyber adversaries. Traditional security mechanisms, which often rely on signature-based threat detection, struggle to address sophisticated attacks such as adversarial AI manipulations, IoT botnet infiltrations, and real-time data breaches. This study examines emerging cybersecurity risks in AI and IoT environments, emphasizing the limitations of existing security frameworks in detecting and mitigating evolving threats. One of the key challenges is the inability of conventional methods to adapt to novel attack patterns in dynamic and complex networks. To address this issue, we introduce a machine learning-based risk assessment framework designed for proactive threat mitigation. This framework leverages anomaly detection, behavioral analytics, and predictive threat modeling to identify potential cybersecurity risks in real time. By integrating adaptive learning algorithms and continuous monitoring, the proposed system enhances resilience against AI-driven cyberattacks and IoT-based vulnerabilities. The findings highlight the critical need for AI-driven cybersecurity solutions capable of evolving alongside emerging threats, ensuring the safety and reliability of interconnected digital ecosystems.
Advancements in 5G-Enabled Industrial IoT: Emerging Applications and Future Research Directions Silitonga, Joe Laksamana
International Journal of Information System and Innovative Technology Vol. 3 No. 1 (2024): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/rs4e8464

Abstract

The emergence of 5G technology has significantly transformed the Industrial Internet of Things (IIoT) by enabling high-speed, low-latency, and ultra-reliable communication. This advancement has paved the way for smarter and more efficient industrial operations, including automated manufacturing, predictive maintenance, and real-time monitoring. By leveraging key features such as network slicing, massive machine-type communication (mMTC), and ultra-reliable low-latency communication (URLLC), industries can enhance operational efficiency, reduce downtime, and optimize resource management. Despite these advantages, several challenges hinder the full-scale deployment of 5G-enabled IIoT. Issues such as scalability constraints, security risks, interoperability challenges, and high infrastructure costs continue to pose barriers. Additionally, integrating 5G with existing industrial systems and ensuring efficient spectrum utilization require innovative solutions. Addressing these concerns necessitates advancements in AI-driven network management, robust cybersecurity frameworks, and optimized communication protocols. This paper presents a comprehensive review of the current landscape of 5G-enabled IIoT, exploring state-of-the-art research on network architectures, edge computing, AI integration, and security mechanisms. Furthermore, it identifies future research directions, emphasizing the role of intelligent networking, autonomous decision-making, and sustainable infrastructure in advancing IIoT applications. The insights provided in this review aim to support researchers and industry practitioners in optimizing 5G-powered IIoT ecosystems.
Big Data Analytics in Financial Statement Analysis: A Systematic Review of Challenges, Techniques, and Future Directions Simatupang, Oktaria
International Journal of Information System and Innovative Technology Vol. 3 No. 1 (2024): June
Publisher : Geviva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63322/jf51dc21

Abstract

The integration of big data analytics has significantly transformed financial statement analysis, enhancing the accuracy and efficiency of financial reporting. Traditional financial analysis methods often rely on structured data and manual interpretation, which can be time-consuming and prone to errors. However, the increasing complexity and volume of financial data demand more advanced analytical approaches to improve decision-making and transparency. As a result, big data analytics has emerged as a powerful tool that utilizes machine learning, predictive modeling, and artificial intelligence to extract meaningful insights from large datasets. Despite its benefits, several challenges hinder the effective implementation of big data analytics in financial statement analysis. These include data integration issues, cybersecurity threats, regulatory compliance complexities, and a lack of expertise in handling big data tools. Furthermore, financial professionals often struggle with interpreting unstructured data sources, such as textual information from financial disclosures and market sentiment. To address these challenges, this review paper examines the role of big data analytics in financial statement analysis, highlighting its methodologies, benefits, and limitations. The study explores various analytical techniques, including predictive analytics, anomaly detection, and sentiment analysis, to improve financial reporting accuracy. Additionally, it discusses future directions for developing automated analytical frameworks and regulatory adaptations that enhance data reliability and security. This paper provides a comprehensive review of existing research, offering valuable insights into how big data analytics is reshaping financial statement analysis and the potential solutions to overcome current challenges.

Page 3 of 4 | Total Record : 35

 1   2   3   4 

Filter by Year

2022 2025


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2025): June Vol. 3 No. 2 (2024): December Vol. 3 No. 1 (2024): June Vol. 2 No. 2 (2023): December Vol. 2 No. 1 (2023): June Vol. 1 No. 2 (2022): December Vol. 1 No. 1 (2022): June