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All Journal Aptisi Transactions on Management Blockchain Frontier Technology (BFRONT) Journal of Computer Science and Technology Application
John Edwards
Pandawan Incorporation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Library & Information Science Other

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Journal : journal of computer science and technology application

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Integrating AI and Big Data to Enhance Performance andSustainability in Hospitality Hasrul Azwar Hasibuan; Syaifuddin; Rusiadi; John Edwards
CORISINTA Vol 2 No 2 (2025): August
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/corisinta.v2i2.118

Abstract

This paper explores the impact of Big Data and Artificial Intelligence (AI) on Employee Performance and Sustainability in the hospitality industry. The paper further explains how integrating Big Data and AI can optimize operations, enhance employee efficiency, and promote sustainable practices. The research uses SmartPLS to analyze the relationships between these variables, with a focus on how Big Data and AI influence Employee Performance, which in turn contributes to Sustainability efforts. The findings, show that both Big Data and AI have significant positive effects on Employee Performance, with Big Data demonstrating a stronger impact. Moreover, Employee Performance mediates the relationship between Big Data, AI, and Sustainability, indicating that improvements in employee performance lead to better sustainability outcomes, such as resource optimization and waste reduction. The study findings align with SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production), highlighting the potential of technology to drive both economic and environmental sustainability in the hospitality sector This research contributes to understanding how the application of Big Data and AI can help hospitality businesses achieve long-term success through improved operational efficiency and sustainable practices.
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.
Co-Authors Aswadi Jaya Danang Surya Budi Desy Apriani Dwi Cahyono Fitra Putri Oganda Hasrul Azwar Hasibuan Maulidya Reva Aprianti Po Abbas Sunarya Ruli Supriati, Ruli Suca Rusdian Syaifuddin Terra Saptina Maulani Tuti Nurhaeni Yansa Sendi Fadillah