Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Journal of Artificial Intelligence and Development Journal of Creative Power and Ambition (JCPA)
Desfita Eka Putri
Politeknik LP3I Pekanbaru, Indonesia
Computer Science & IT Economics, Econometrics & Finance Environmental Science Mathematics Social Sciences Other

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

Found 2 Documents
Search

 1 
Optimization of Deep Learning Algorithms for Medical Image Detection in Cloud Computing-Based Health Applications Desfita Eka Putri; Santi Prayudani; Joni Wilson Sitopu
Journal of Artificial Intelligence and Development Vol. 2 No. 01 (2024): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70610/jaid.v4i1.702

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.
Design and Development of a Lecturer Research Information System at Politeknik LP3I Pekanbaru Desfita Eka Putri
Journal of Creative Power and Ambition (JCPA) Vol. 4 No. 02 (2026): Journal of Creative Power and Ambition (JCPA)
Publisher : CV Edujavare Publishing

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

Abstract

The development of information technology has a major impact on the speed and accuracy of data management in various fields, including lecturer research management. LP3I Pekanbaru Polytechnic experienced challenges in processing research data that were still manual and scattered, resulting in duplication of data, loss of research documents, and difficulty in monitoring research progress. This research aims to design and build an integrated, web-based, and Lecturer Research Information System that is able to support the efficient processing of research data. The research method used is Research and Development (R&D) with a Prototyping approach. The results of the research are in the form of an information system that includes research registration modules, fund submissions, progress monitoring, and publication of research results. The system also comes with a Use Case Diagram, Activity Diagram, and Sequence Diagram to support implementation. This system is expected to be able to improve the efficiency of lecturer research management and become an accurate and well-integrated research database.
Co-Authors Joni Wilson Sitopu Santi Prayudani