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Asfahani Asfahani
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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 4 Documents
 1 
Search results for , issue "Vol. 4 No. 1 (2025): Journal of Artificial Intelligence and Development" : 4 Documents clear
Industrial Revitalization with AI between Opportunities and Challenges for Global Economic Growth Loso Judijanto; Asfahani Asfahani; Anjana Prusty; Nova Krisnawati; Asri Ady Bakri
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

This study investigates the Industrial Revitalization with AI, exploring both the opportunities and challenges it presents for global economic growth. The aim is to provide a comprehensive understanding of AI's role in reshaping industries, the methods used in the study include qualitative research methods such as in-depth interviews with industry leaders, technology experts, and stakeholders. The findings highlight the significant potential of AI in enhancing productivity and innovation across various sectors, while also emphasizing the need for addressing concerns such as job displacement, data privacy, and regulatory frameworks. In conclusion, the study underscores the transformative impact of AI on industrial revitalization, emphasizing the importance of responsible AI deployment and collaborative efforts for sustainable economic growth.
AI-Supported Management through Leveraging Artificial Intelligence for Effective Decision Making Loso Judijanto; Asfahani Asfahani; Asri Ady Bakri; Edy Susanto; Ummu Kulsum
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

This study explores integrating artificial intelligence (AI) technology in management practices to improve decision-making effectiveness. This research investigates how AI-powered management systems leverage machine learning, predictive analysis, and scenario modeling to provide real-time data insights and optimize resource allocation. This research uses the Systematic Literature Review (SLR) methodology to analyze existing studies and theoretical frameworks related to AI in management. The research results reveal that AI technology contributes significantly to strategic agility, operational efficiency, and risk assessment, ultimately resulting in better decision outcomes. The study concludes that organizations must invest in talent development, ethical considerations, and cybersecurity measures to fully exploit AI's potential for effective decision-making in today's dynamic business landscape.
Optimization of Organizational Performance by Utilization of AI for Strategic Management Insights Loso Judijanto; Asfahani Asfahani; Syamsul Muqorrobin; Nova Krisnawati
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

In response to the complexities of modern markets, organizations are increasingly turning to Artificial Intelligence (AI) as a transformative tool for enhancing strategic decision-making and organizational performance. This study investigates the impact of AI utilization on optimizing organizational performance within strategic management. A qualitative research method was employed, utilizing semi-structured interviews to gather insights from key organizational informants. The findings highlight the significant role of organizational culture, leadership support, and collaborative approaches in maximizing AI's potential for agility, informed decision-making, and competitive advantage. The analysis contributes to a deeper understanding of AI's implications for strategic management and underscores the importance of holistic approaches in leveraging AI for sustained performance enhancement.
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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