Pengembangan Model PACS Server Post Processing Terintegrasi â€œCLEMAGSâ€ untuk Meningkatkan Keefektifan Pembelajaran Klinis pada Program Studi Imaging Diagnostik Program Pascasarjana Poltekkes Kemenkes Semarang - Pranandya, Brian Ilham; Wibowo, Gatot Murti; Suwondo, Ari; Anwar, M. Choiroel; Darmini, Darmini (Poltekkes Kemenkes Semarang, 2025)
Background: One aspect of digital transformation in education today is online learning or commonly referred to as E-learning. Clinical Education Management System or CLEMAGS, is an application system used for clinical education management in the field of web-based Diagnostic Imaging. The development of Clinical Learning Management System (CLEMAGS), which was built with the help of Orthanc Platform, is to create a more inventive and interactive learning model for students of Postgraduate Diagnostic Imaging study program of Poltekkes Kemenkes Semarang. Designing and developing the PACS Server Post Processing model in CLEMAGS to support activities in the Diagnostic Imaging Postgraduate Program of the Poltekkes Kemenkes Semarang and testing the effectiveness of the PACS Server Post Processing model in CLEMAGS in the Diagnostic Imaging Postgraduate Program of the Poltekkes Kemenkes Semarang.Methods: This type of research is Research and Development (R&D) which uses the Rapid Application Development (RAD) research approach method. Analysis of differences in pre-test and post-test data using paired sample t-test. While analyzing the increase in effectiveness test results using the N-Gain Test.Results: The paired sample t-test results are sig. value of 0.003 (p-value <0.05), which means that there is a significant difference between the pre-test data and the post-test implementation of the PACS Server Post Processing Model in CLEMAGS. Overall, the average N-Gain score is 0.89 and is included in the â€œHighâ€ understanding improvement category. The percentage of N-Gain obtained was 89.94% and included in the effective category.Conclusions: The PACS Server Post Processing model in CLEMAGS is effective in improving user performance in managing medical imaging data. The TRIP-ID system is a medical image-based learning solution that can be practically applied in health education institutions. Further development can be directed toward the integration of artificial intelligence (AI) for automatic interpretation, as well as expanding implementation in various other educational institutions.