Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal Aptisi Transactions on Management Journal of Computer Science and Technology Application
Agung Lorenzo
Eduaward Incorporation
Computer Science & IT Decision Sciences, Operations Research & Management Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : journal of computer science and technology application

 1 
Artificial Intelligence and Big Data Framework for Cybersecurity Resilience in Distributed Networks Syaifuddin Syaifuddin; Ahmad Gunawan; Maulana Arif Komara; Agung Lorenzo
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kjn69m96

Abstract

The rapid expansion of distributed computer networks, driven by cloud computing, IoT ecosystems, edge computing, and software-defined infrastructures, has increased cybersecurity complexity. The growing volume, velocity, and variety of network data challenge traditional security mechanisms that focus primarily on threat detection, often neglecting system resilience, adaptive response, and recovery. This study develops a resilience-oriented intelligent big data analytics framework integrating Artificial Intelegence (AI), big data processing, and distributed cybersecurity monitoring to strengthen resilience in modern digital environments. A qualitative approach was employed through systematic literature review, conceptual modeling, thematic synthesis, and comparative analysis of existing architectures. The framework consists of four interconnected layers: data acquisition and aggregation, big data processing, intelligent analytics, and adaptive response and recovery. It supports continuous monitoring, anomaly detection, threat prediction, automated mitigation, and recovery orchestration. Comparative analysis indicates that prior studies focus mainly on improving intrusion detection or machine learning techniques, providing limited attention to resilience dimensions such as adaptability, fault tolerance, recovery efficiency, and operational stability. In contrast, the proposed framework integrates intelligent analytics with scalable big data infrastructures and distributed security mechanisms to create a unified resilience-oriented cybersecurity ecosystem. Findings suggest that combining AI-driven analytics, distributed processing, and adaptive security orchestration provides a strategic foundation for enhancing cybersecurity resilience, supporting sustainable digital infrastructure development, and ensuring operational stability in increasingly complex and interconnected network environments.
Challenges and Opportunities in Implementing Big Data for Small and Medium Enterprises (SMEs) Dwi Cahyono; Apriani Sijabat; Muktar Bahruddin Panjaitan; Dwi Julianingsih; Agung Lorenzo
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.v2i1.74

Abstract

Small and Medium Enterprises (SMEs) play a crucial role in the global economy but often face significant challenges when adopting new technologies like Big Data. While Big Data offers opportunities for improving decision-making, operational efficiency, and gaining a competitive edge, many SMEs struggle due to financial constraints, limited technical expertise, and concerns over data security and privacy. This paper explores the challenges SMEs encounter in adopting Big Data and identifies the opportunities it provides for growth and innovation. A mixed-methods approach is employed, combining qualitative interviews with SME managers and quantitative surveys from 150 SMEs to gather comprehensive data. The findings reveal that SMEs face barriers such as high implementation costs and lack of skilled personnel, but they also recognize the potential for Big Data to enhance customer insights, improve business processes, and foster new business models. Recommendations include exploring cost-effective solutions, investing in employee training, strengthening data security, and adopting modular systems that integrate easily with existing operations. This study underscores the importance of overcoming these challenges and leveraging Big Data as a key driver of digital transformation for SMEs, ultimately helping them to compete more effectively in an increasingly data-driven marketplace.
Co-Authors Ahmad Gunawan Apriani Sijabat Dwi Cahyono Dwi Julianingsih Mardiana Maulana Arif Komara Muhamad Yusup Muktar Bahruddin Panjaitan Otniel Feliks Putra Wahyudi Rafika, Ageng Setiani Syaifuddin Syaifuddin