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Journal of Computer Science and Technology Application
Published by Pandawan Sejahtera Indonesia
ISSN : 30467616     EISSN : 30643597     DOI : https://doi.org/10.33050
Core Subject : Science, Education,
Computer Science & IT Other
The Journal of Computer Science and Technology Application (CORISINTA) is an international, open-access journal dedicated to advancing Information and Communication Technology (ICT). CORISINTA publishes research in Artificial Intelligence, Big Data, Cybersecurity, and Computer Networks. Through its rigorous double-blind peer-review process, the journal ensures the highest standards of quality. CORISINTA actively supports the United Nations Sustainable Development Goals (SDGs), including SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 17 (Partnerships for the Goals).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 57 Documents
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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.
Deep Learning Enabled Security Monitoring for Intrusion Detection in Smart Campus Networks Ruli Supriati; Nuke Puji Lestari Santoso; Steven Harazaki Lase; Carlos Perez
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/a88eeq63

Abstract

The increasing complexity of smart campus networks has heightened the need for advanced cybersecurity measures to protect sensitive data and ensure seamless operations. Traditional Intrusion Detection Systems (IDS) often struggle to cope with the dynamic and heterogeneous nature of network traffic in smart campus environments, necessitating the development of more effective solutions. This study aims to propose a deep learning-based intrusion detection system for smart campus networks, utilizing a Hybrid CNN-LSTM model to enhance security monitoring. The proposed methodology integrates Convolutional Neural Networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for capturing temporal dependencies in network traffic. The model was trained and evaluated on a combination of publicly available datasets and simulated smart campus data, measuring performance through key metrics such as accuracy, precision, recall, and F1-score. Results show that the Hybrid CNN-LSTM model outperforms traditional machine learning models, achieving an accuracy of 97.3% and a ROC-AUC of 0.99, demonstrating superior detection of both known and unknown intrusions. The findings suggest that deep learning models, especially when tailored to smart campus contexts, offer significant advantages in real-time threat detection and adaptive learning. This research contributes to the growing body of knowledge on AI-driven network security and provides practical insights for improving cybersecurity infrastructures in higher education institutions.
Advanced Big Data Analytics for Proactive Cyber Threat Mitigation in Large Scale Computer Networks Ratna Tri Hari Safariningsih; Untung Rahardja; Mitra Terima Des Sincer Putri; Nur Azizah; Ethan Harris
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kr1b0a59

Abstract

The rapid expansion of digital infrastructure and interconnected systems, large scale computer networks increasingly face sophisticated cyber threats that challenge traditional security mechanisms. The growing volume, velocity, and variety of network data require more advanced analytical approaches capable of detecting and mitigating threats proactively. Over recent years, artificial intelligence and big data technologies have demonstrated strong potential in improving the efficiency and accuracy of cybersecurity systems, particularly in environments characterized by high data complexity and dynamic attack patterns. Motivated by these challenges, this study proposes an advanced big data analytics approach integrated with artificial intelligence techniques to support proactive cyber threat mitigation in large scale computer networks. The proposed method processes large scale network traffic data using intelligent analytical models capable of identifying abnormal behavioral patterns and predicting potential cyber attacks before they fully develop. Experimental simulations using benchmark network datasets indicate that the proposed approach improves detection accuracy, reduces false alarm rates, and enhances the responsiveness of cybersecurity systems when compared with several conventional analytical techniques. The integration of scalable big data processing with adaptive artificial intelligence models also demonstrates strong capability in handling complex and high volume network environments. The findings highlight that advanced big data analytics combined with artificial intelligence can significantly strengthen proactive cyber defense mechanisms in modern computer networks, contributing to more resilient and adaptive cybersecurity infrastructures capable of responding to evolving digital threats.
Orchestrating Big Data and Artificial Intelligence for Adaptive Digital Business Strategy Marviola Hardini; Sheila Aulia Anjani; Sherli Triandari; Fhia Amelia; Marta Rodriguez
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/qx8e0j55

Abstract

The rapid acceleration of digital transformation has changed the way organizations formulate and implement business strategies, requiring firms to become more adaptive, data-driven, and responsive to dynamic market conditions. This study aims to examine how big data and artificial intelligence can be orchestrated as integrated strategic capabilities to support adaptive digital business strategy. Using a qualitative conceptual approach, this study applies a structured literature review and thematic synthesis to analyze previous studies related to big data capability, artificial intelligence capability, governance mechanisms, intelligent business insight, and strategic adaptability. The results show that big data functions as a strategic foundation by providing diverse information from customers, markets, operations, and digital platforms, while artificial intelligence acts as an intelligent decision engine that transforms data into predictions, recommendations, automation, and actionable business insights. The findings also indicate that governance and human decision-making are essential in ensuring that the use of big data and AI remains reliable, transparent, accountable, secure, and aligned with organizational objectives. This study concludes that adaptive digital business strategy emerges from the continuous orchestration of data resources, AI systems, governance structures, human judgment, and strategic execution. The proposed framework contributes to digital business literature by explaining how AI-driven big data orchestration can improve decision quality, agility, competitiveness, innovation, operational efficiency, and sustainable digital value creation. In addition, the discussion is expanded to include cybersecurity, data privacy, secure data processing, and AI risk management as critical enablers of large-scale data-driven business systems.
Secure Communication in Solar Panel Monitoring Systems Using SmartPLS Ageng Setiani Rafika; Dhimas Tribuana; Rohim Rohim; Agung Rizky; Chua Toh Hua
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/gtmmg783

Abstract

This study investigates the factors shaping secure communication architecture in solar panel monitoring systems within smart grid environments, focusing on cybersecurity, data integrity, and system reliability. The integration of solar photovoltaic systems with digital monitoring networks enhances real-time energy management but introduces communication vulnerabilities affecting monitoring accuracy and operational stability. This study aims to assess how Network Security, Data Integrity, and System Reliability contribute to Secure Communication Architecture and Monitoring Performance. A quantitative research design was employed, and the proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS. The research model included five constructs: Network Security, Data Integrity, System Reliability, Secure Communication Architecture, and Monitoring Performance. Results indicate that Network Security significantly and positively affects Secure Communication Architecture, while Data Integrity positively influences both Secure Communication Architecture and System Reliability. Furthermore, Secure Communication Architecture and System Reliability both contribute positively to Monitoring Performance. The measurement model demonstrated acceptable reliability and validity, confirming construct robustness. These findings highlight that effective solar panel monitoring systems depend not only on technical energy infrastructure but also on secure, reliable communication networks that ensure accurate and timely data exchange. The study concludes that strengthening cybersecurity mechanisms, data integrity controls, and communication reliability is essential to improve monitoring performance and support resilient smart grid-based renewable energy management.
Self Learning AI and Big Data for Resilient Cybersecurity in Distributed Networks Aswadi Jaya; Suca Rusdian; Fitra Putri Oganda; Tuti Nurhaeni; John Edwards
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kz2w4z84

Abstract

The rapid expansion of large scale computer networks driven by cloud infrastructures, Internet of Things environments, and distributed digital services has significantly increased the complexity of cybersecurity threats. Traditional rule based security systems often struggle to detect evolving and previously unseen attacks within high volume network traffic. This study proposes a self learning artificial intelligence approach designed to enhance threat detection capability in large scale computer networks by leveraging adaptive learning mechanisms and large scale network data analysis. The proposed framework integrates machine learning models with big data processing techniques to continuously learn from network traffic patterns, behavioral anomalies, and historical security events. Through automated feature extraction and iterative model refinement, the system dynamically improves its ability to identify malicious activities without relying solely on predefined signatures. This study adopts a qualitative conceptual evaluation approach to examine the proposed self-learning artificial intelligence and big data framework for cybersecurity resilience in distributed computer networks. The evaluation is conducted through literature synthesis, comparative analysis of existing intrusion detection approaches, architectural modeling, and conceptual validation of the proposed framework against key cybersecurity requirements, including adaptability, scalability, continuous learning, and detection coverage for known and unknown threats. The system also shows strong scalability in processing high volume network data while maintaining stable detection performance. These results indicate that integrating self learning artificial intelligence with scalable data processing can strengthen cybersecurity resilience in large scale computer networks and support the development of more adaptive and intelligent network defense mechanisms for future digital infrastructures.
Big Data Governance Framework for Trustworthy Artificial Intelligence Decision Systems Adam Faturahman; Alfri Adiwijaya; Ardivan Avandi; Nanda Septiani; Kristina Vaher
CORISINTA Vol 3 No 2 (2026): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/kp2fkq63

Abstract

The rapid adoption of Artificial Intelligence (AI) decision systems has increased organizational dependence on large-scale data, making big data governance a critical requirement for ensuring reliable and responsible decision-making. Although AI systems are often evaluated based on predictive accuracy and computational performance, their trustworthiness is strongly influenced by the quality, security, privacy, traceability, and fairness of the data used throughout the AI lifecycle. This study aims to develop a Big Data Governance Framework for Trustworthy AI Decision Systems by integrating key governance dimensions with trustworthy AI requirements. A qualitative conceptual framework development approach was employed, supported by structured literature review, thematic synthesis, and design science research principles. Relevant literature on big data governance, trustworthy AI, data quality, privacy, security, explainability, accountability, fairness, and AI decision systems was reviewed to identify recurring concepts and research gaps. The results show that trustworthy AI decision systems require seven core governance dimensions: data quality governance, security and privacy governance, metadata and data lineage, bias and fairness control, explainability support, accountability mechanisms, and continuous monitoring. These dimensions strengthen trustworthy AI capabilities, including reliability, transparency, explainability, fairness, privacy preservation, security, robustness, and auditability. The proposed framework demonstrates that trustworthy AI is not only determined by algorithmic performance but also by strong data governance across the AI lifecycle. This study concludes that effective big data governance can improve decision accuracy, traceability, accountability, risk reduction, and stakeholder trust in AI-based decision systems.

Page 6 of 6 | Total Record : 57

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