Enhancing AI Model Accuracy and Scalability Through Big Data and Cloud Computing - Jamaludin, Haris; Achlison, Unang; Rokhman, Nur (Journal of Technology Informatics and Engineering, 2024)
Data's exponential growth and cloud computing advancements have significantly impacted artificial intelligence (AI) model development. This study investigates how big data techniques integrated with cloud computing enhance the scalability and accuracy of AI models across sectors such as healthcare, business, and cybersecurity. Adopting a qualitative methodology, the research examines secondary data from 2020–2024, including case studies and literature. Key findings reveal that cloud computing enables large-scale data processing with significant efficiency, achieving average speeds of 20–45 seconds for datasets ranging from 50–120 TB/day. AI model accuracy also improved across sectors, increasing by 20% on average—reaching 92% in cybersecurity, 90% in healthcare, and 85% in business applications. The study identifies deep learning algorithms as pivotal for leveraging cloud computing's flexibility, allowing for advanced data analysis and real-time insights. However, challenges in data security and privacy remain critical concerns. This research contributes by highlighting the transformative role of cloud computing in big data management and AI optimization, offering practical insights into enhancing predictive capabilities while addressing operational cost efficiency through scalable infrastructure. The findings emphasize the necessity of robust security protocols to mitigate risks and ensure sustainable AI applications. Future research should explore sector-specific implementations to refine and expand the practical utility of these integrated technologies.