Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal ENDLESS : International Journal of Future Studies
Irawaty
Universitas Gunadarma, Indonesia
Economics, Econometrics & Finance Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Bridging the Gap: Exploring the Role of Computer Science in Enhancing Interactive and Inclusive Learning Environments Budi Utami Fahnun; Eel Susilowati; Darmastuti; Hadyan Mardhi Fadlillah; Irawaty
ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES Vol. 6 No. 3 (2023): ENDLESS: International Journal of Future Studies
Publisher : Global Writing Academica Researching & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54783/endlessjournal.v6i3.211

Abstract

This article explores the transformative role of computer science in shaping interactive and inclusive learning environments in the educational sector. With the rapid advancement of technology, computer science has become a pivotal element in redefining educational methodologies, facilitating personalized and accessible learning experiences for a diverse student population. This study employs a mixed-methods approach, integrating quantitative data from educational technology usage surveys with qualitative insights from interviews with educators and students. We examine the implementation of computer science tools such as adaptive learning platforms, virtual and augmented reality, and AI-driven educational software, assessing their impact on student engagement, learning outcomes, and inclusivity. Our findings reveal that these technologies not only enhance interactive learning experiences but also significantly contribute to the inclusivity of education by providing tailored learning paths and overcoming traditional barriers. The study highlights the potential of computer science to democratize education, making it more equitable and accessible to learners with varying needs and backgrounds. Furthermore, we discuss the challenges and opportunities in integrating these technologies into existing educational frameworks, offering recommendations for educators, policymakers, and technology developers. This article contributes to the growing body of research on the intersection of computer science and education, providing insights into the future of learning in an increasingly digital world.
Utilizing Machine Learning for Anomaly Detection in Cybersecurity Systems Budi Utami Fahnun; Eel Susilowati; Hadyan Mardhi Fadlillah; Irawaty
ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES Vol. 7 No. 2 (2024): ENDLESS: International Journal of Future Studies
Publisher : Global Writing Academica Researching & Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Anomalies in cybersecurity systems are increasingly complex and sophisticated, making detection difficult using traditional rule-based and signature-based approaches. In facing these challenges, machine learning is crucial to improve real-time anomaly detection capabilities. This study aims to explore the role of machine learning in detecting anomalies in cybersecurity systems. The research method is carried out using a qualitative approach, collecting data from relevant literature and interviews with experts in the fields of cybersecurity and machine learning. The results of this study indicate that machine learning can effectively improve the ability of cybersecurity systems to detect and respond to threats more quickly and accurately. Implementing machine learning allows for deeper analysis of complex cybersecurity data, recognizing unexpected anomalous patterns, and adapting to new attacks. Despite challenges such as data variability and dynamic operational environments, the evaluation of model performance shows significant progress in protecting information systems from increasingly complex threats. The future of anomaly detection in cybersecurity promises the possibility of developing more sophisticated technologies, strengthening defenses against evolving threats, and improving overall security.
Co-Authors Budi Utami Fahnun Darmastuti Eel Susilowati Hadyan Mardhi Fadlillah