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All Journal Journal of Computer Science Application and Engineering Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK)
Ahmad, Asiyah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Other

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Journal : Journal of Computer Science Application and Engineering

 1 
Improving Distance Learning Security using Machine Learning Ahmad, Asiyah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 2 (2023): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v1i2.13

Abstract

This study explores the intersection of machine learning and distance learning security, aiming to fortify online educational platforms amidst the evolving digital landscape. With technological advancements fueling the rise of distance learning, concerns regarding cybersecurity in virtual educational environments have grown significantly. The fusion of machine learning and distance learning security represents a proactive approach to bolstering safety and integrity within virtual classrooms. Leveraging sophisticated algorithms, this amalgamation seeks to preempt security breaches by identifying irregular patterns, addressing vulnerabilities, and swiftly countering risks like phishing attempts and data breaches. By utilizing historical data and real-time monitoring, machine learning models offer predictive capabilities, enabling educational institutions to anticipate emerging threats and safeguard the learning process while ensuring data integrity and user privacy. While machine learning techniques, such as anomaly detection and predictive modeling, have shown promise in fortifying security measures, ethical considerations and collaborative efforts are essential for responsible implementation. This comprehensive study, involving literature review, knowledge enrichment, case studies, and informed conclusions, aims to guide further research and practical applications in enhancing distance learning security through machine learning.
Empowering Education: Cloud Solutions for Remote Schools in Indonesia Ahmad, Asiyah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 2 No. 1 (2024): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v2i1.23

Abstract

This article explores the transformative potential of cloud solutions in addressing the educational challenges faced by remote and underserved regions in Indonesia. The introduction highlights the significance of technology integration in education and emphasizes the obstacles Indonesia encounters in delivering quality education to remote areas. Cloud solutions emerge as a promising avenue, providing scalable tools to bridge the urban-rural learning gap. The literature review delves into the transformative impact of cloud-based education, citing studies that emphasize enhanced student engagement and personalized learning experiences. It underscores the importance of factors such as infrastructure development, internet connectivity, and digital literacy, essential for the success of cloud-based education in remote areas. Cultural considerations are also discussed, emphasizing the need to align technology with local values. The methodology section outlines the research steps, including a literature review, real-world implementation examples, exploration of potential benefits, and practical suggestions for policymakers and educators. Real examples of successful cloud technology implementations illustrate the positive outcomes, ranging from improved access to educational resources to streamlined administrative processes. The author summarizes the potential of cloud solutions, highlighting aspects such as accessibility, scalability, cost-effectiveness, collaboration, and personalized learning.
Enhancing Hospital Efficiency through IoT and AI: A Smart Healthcare System Ahmad, Asiyah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 2 No. 2 (2024): JOSAPEN - July
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v2i2.36

Abstract

In the rapidly evolving healthcare landscape, the integration of Internet of Things (IoT) and Artificial Intelligence (AI) is transforming hospital efficiency. This study explores how these technologies can enhance hospital operations by optimizing resource management, improving patient care, and reducing operational costs. IoT devices enable real-time monitoring of patient health and hospital assets, facilitating timely interventions and maintenance. Concurrently, AI-driven analytics improve decision-making processes by predicting patient needs and optimizing resource allocation. The synergy between IoT and AI creates a smart healthcare system that offers advanced data processing and actionable insights, leading to improved patient outcomes. Despite challenges such as data privacy concerns and infrastructure investments, the potential benefits of IoT and AI in healthcare are substantial. This paper presents a comprehensive framework for integrating these technologies into hospital operations, highlighting their impact on efficiency and patient care. The findings suggest that IoT and AI can significantly enhance hospital performance, paving the way for a smarter healthcare system.
Optimizing the Traveling Salesman Problem Using Machine Learning and Predictive Algorithms Ahmad, Asiyah
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 3 No. 1 (2025): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v3i1.46

Abstract

The Traveling Salesman Problem (TSP) is a foundational challenge in optimization, with applications in logistics, routing, and scheduling. Traditional algorithms such as dynamic programming and brute-force search guarantee optimal solutions but become computationally expensive as the number of cities grow, hindering scalability. Consequently, research has shifted towards machine learning (ML) and predictive algorithms, which show promise in approximating optimal solutions more efficiently. This study aims to optimize TSP using ML models, specifically focusing on enhancing scalability and minimizing computational overhead. The approach incorporates techniques like reinforcement learning (RL) and graph neural networks (GNNs), leveraging their ability to learn and generalize from smaller problem instances. The primary contribution of this work is an ML-driven framework for TSP, which demonstrates improved efficiency and adaptability compared to traditional algorithms. Evaluation metrics, including total path length, convergence time, and optimality gap, validate the model's effectiveness, achieving optimal paths with reduced execution time. This research offers a practical ML-based solution for TSP that balances accuracy with computational speed, providing a feasible alternative for large-scale and dynamic real-world applications.
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