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All Journal Technomedia Journal ADI Pengabdian kepada Masyarakat Jurnal (ADIMAS Jurnal) Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
Dwi Cahyono
University of Muhammadiyah Jember
Humanities Computer Science & IT Control & Systems Engineering Education Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

Published : 6 Documents Claim Missing Document
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Journal : journal of computer science and technology application

 1 
Self Learning Artificial Intelligence for Autonomous Threat Detection in Computer Networks Dwi Cahyono; Herman Herman; Ikyboy Van Versie
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tk5ypk40

Abstract

The rapid expansion of large-scale computer networks and the exponential growth of big data have significantly increased the complexity and frequency of cyber threats, rendering traditional signature-based security mechanisms inadequate for adaptive detection. This study aims to develop a self-learning AI model capable of autonomously identifying evolving attack patterns and anomalous behaviors in large-scale networks without relying exclusively on pre-labeled datasets. The proposed framework integrates deep neural architectures, incremental learning, and behavior-based traffic analysis to enable continuous adaptation to dynamic threat environments while ensuring computational efficiency and scalability. The model was trained and evaluated using realistic network traffic datasets simulating distributed attacks, zero-day exploits, and advanced persistent threats across heterogeneous environments. Experimental findings demonstrate that the self-learning approach enhances detection accuracy, reduces false positives, and accelerates response times compared to conventional intrusion detection systems. In addition, the combination of deep neural architectures with incremental learning and scalable data processing further strengthens model robustness and adaptability in complex and evolving networks. The results indicate that integrating adaptive AI into cybersecurity frameworks enhances proactive defense capabilities, improves resilience in large-scale computer networks, and provides a scalable, intelligent solution for next-generation threat detection systems. This study highlights the practical relevance of combining AI, big data analytics, and cybersecurity strategies to support intelligent, adaptive security solutions capable of addressing emerging threats, minimizing operational risks, and fostering robust network protection in increasingly complex digital infrastructures.
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.
Efficient Machine Learning Acceleration with Randomized Linear Algebra for Big Data Dwi Cahyono; Apriani Sijabat; Kamal Arif Al-Farouqi
CORISINTA Vol 3 No 1 (2026): February
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/v3i1.163

Abstract

The rapid growth of big data has significantly increased the computational complexity of machine learning models, particularly due to intensive linear algebra operations that limit scalability and efficiency. This study aims to investigate the effectiveness of Randomized Linear Algebra (RLA) as an acceleration strategy for machine learning in large scale data environments. The research adopts an experimental methodology by integrating randomized techniques such as matrix sketching and random projection into standard machine learning pipelines and evaluating their performance against deterministic baseline approaches. Experiments are conducted on large dimensional datasets using multiple machine learning models, with performance assessed in terms of computational time, memory usage, model accuracy, and scalability. The results demonstrate that the proposed RLA based approach substantially reduces computational cost and memory consumption while maintaining comparable predictive accuracy to conventional methods. These findings indicate that randomized techniques provide an effective trade off between efficiency and accuracy, enabling scalable machine learning for big data applications. In conclusion, this study contributes to the advancement of efficient Artificial Intelligence (AI) systems by demonstrating that RLA can serve as a practical and scalable solution for accelerating machine learning computations in big data contexts, aligning with the growing demand for resource efficient and high performance AI infrastructures.
Co-Authors Agung Lorenzo Apriani Sijabat Apriani Sijabat Ardivan Avandi Arthur Simanjuntak Chua Toh Hua Dwi Julianingsih Herman Herman Ikyboy Van Versie Indri Mariska Putri John Edwards Kamal Arif Al-Farouqi Maulidya Reva Aprianti Mohamad Agus Salim Muktar Bahruddin Panjaitan Nova Syahrani Arasid Rohim Rohim Suhada Suhada Terra Saptina Maulani Yansa Sendi Fadillah