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The Journal of Artificial Intelligence and Development
Published by Edujavare Publishing
ISSN : -     EISSN : 30317428     DOI : https://doi.org/10.xxx/
Core Subject : Science, Education, Social,
Computer Science & IT Environmental Science Mathematics Social Sciences Other
The Journal of Artificial Intelligence and Development (e-ISSN: 3031-7428) is dedicated to the rapid dissemination of important research results to the global artificial intelligence (AI) community. The journal’s scope encompasses all areas of AI, including agents and multi-agent systems, automated reasoning, constraint processing and search, knowledge representation, machine learning, natural language, planning and scheduling, robotics and vision, and uncertainty in AI.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 23 Documents
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Data Security Analysis in AI Systems: Risks and Protection Strategies in the Digital Era Judijanto, Loso
Journal of Artificial Intelligence and Development Vol. 2 No. 1 (2023): AI Development
Publisher : Edujavare Publishing

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Abstract

This research focuses on the analysis of data security risks in Artificial Intelligence (AI) systems, particularly in the context of the growing challenges posed by the digital era. With the increasing reliance on AI for processing sensitive data, vulnerabilities such as adversarial attacks, privacy violations, and data breaches have become significant concerns. The primary objective of this study is to identify these risks, evaluate existing protection strategies, and propose effective solutions to enhance data security in AI systems. A mixed-methods approach was employed, combining a comprehensive literature review with qualitative and quantitative data collection, including case studies, expert interviews, and statistical analysis of AI security incidents. The results revealed that while traditional security measures like encryption and access control are essential, they are insufficient to address the unique risks posed by AI technologies. Emerging techniques such as federated learning, differential privacy, and adversarial training were found to offer promising solutions but face challenges in terms of implementation and model accuracy. The research concluded that a holistic approach, integrating both traditional cybersecurity practices and AI-specific strategies, is necessary to safeguard sensitive data in AI systems. This study contributes to the field by offering practical insights into current AI security issues and proposing recommendations for improving data protection mechanisms. Future research should focus on enhancing the scalability and efficiency of these protection strategies to ensure their effective application in diverse real-world AI systems.
The Role of Natural Language Processing (NLP) in Advancing Language Learning Technology in Educational Platforms Judijanto, Loso
Journal of Artificial Intelligence and Development Vol. 2 No. 2 (2023): AI Deevelopment
Publisher : Edujavare Publishing

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Abstract

This research explores the role of Natural Language Processing (NLP) in enhancing language learning technologies within educational platforms. With the increasing reliance on digital tools for language education, NLP technologies offer promising solutions for personalizing learning, providing real-time feedback, and improving learner engagement. The study aims to investigate how NLP is applied in language learning platforms, identify its benefits and challenges, and explore its potential for further development. Using a qualitative approach, the research includes case studies of popular language learning platforms such as Duolingo and Babbel, and interviews with educators, language learners, and platform developers. Thematic analysis was employed to examine the data, identifying key themes such as personalized learning, learner engagement, and conversational simulation. The findings indicate that NLP significantly enhances personalized learning experiences by adapting content to individual learner needs and providing immediate feedback, which improves learner retention and motivation. However, challenges related to the system's ability to capture linguistic nuances and regional variations, as well as concerns about data privacy, were also identified. This study concludes that while NLP has great potential to transform language education, there are still limitations that need to be addressed. Future research should focus on refining NLP algorithms to handle complex language structures and cultural contexts, as well as addressing ethical concerns regarding data security. The study contributes valuable insights for educators, developers, and policymakers looking to integrate NLP into language learning platforms.
Optimization of Deep Learning Algorithms for Medical Image Detection in Cloud Computing-Based Health Applications Putri, Desfita Eka; Prayudani, Santi; Sitopu, Joni Wilson
Journal of Artificial Intelligence and Development Vol. 4 No. 1 (2025): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

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Abstract

The integration of deep learning into cloud-based healthcare systems has opened new frontiers in medical image analysis, enabling faster, more accurate, and accessible diagnostics. However, the high computational demands of conventional deep learning models pose significant challenges for deployment in cloud environments, especially in latency-sensitive and resource-limited settings. This study aims to optimize deep learning algorithms to enhance their efficiency and scalability for medical image detection within cloud computing infrastructures. A quantitative research approach was employed, involving algorithmic optimization techniques such as pruning, quantization, transfer learning, and federated learning. The models were tested using benchmark medical image datasets and deployed in a simulated cloud environment to evaluate performance metrics such as accuracy, inference time, resource usage, and privacy compliance. Results showed that optimized models, particularly EfficientNet with pruning and quantization, achieved high diagnostic accuracy (up to 91.7%) while significantly reducing computational overhead. Federated learning proved effective in maintaining data privacy with minimal loss in accuracy. The findings suggest that lightweight, secure, and fast deep learning models can be realistically integrated into cloud-based healthcare applications. This study contributes a framework for efficient and scalable AI deployment in clinical settings, particularly in underserved or remote areas.

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