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Rijois Iboy Erwin Saragih
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rijoissaragih@gmail.com
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rijoissaragih@gmail.com
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Jl. Karya Bakti Gg. Dame No. 95, kelurahan Indra Kasih, Kecamatan Medan Tembung, Sumatera Utara
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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 5 Documents
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Search results for , issue "Vol. 3 No. 1 (2024): June" : 5 Documents clear
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
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.

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