Purpose: This study investigates the effects of Artificial Intelligence (AI) use and Customer Experience (CX) in telemedicine services on customer satisfaction, with service duration as a moderating variable. Methods: A quantitative approach was applied using survey data from 121 active telemedicine users. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4, including measurement model evaluation, structural analysis, and moderation testing. Results: The results show that AI use has a positive but insignificant effect on customer satisfaction. In contrast, Customer Experience has a positive and significant effect on customer satisfaction, indicating its central role in telemedicine services. Service duration significantly and negatively moderates the relationship between AI use and customer satisfaction, suggesting that longer AI-based service processes reduce satisfaction. However, service duration does not significantly moderate the relationship between Customer Experience and customer satisfaction. Conclusion: Customer satisfaction in telemedicine is influenced more by experiential quality than by AI adoption alone. Effective AI implementation should emphasize service efficiency to enhance satisfaction. Limitation: This study is limited to a single telemedicine platform and uses a cross-sectional design, which may limit generalizability. Contribution: This research highlights the importance of Customer Experience and demonstrates the conditional effect of service duration on AI-driven telemedicine satisfaction.

Purpose: This study aims to develop a traffic density prediction system in Bandar Lampung City to address increasing congestion caused by the rapid growth of vehicles that exceeds road capacity. The system is intended to support real-time monitoring, improve traffic management efficiency, and facilitate data-driven decision-making for adaptive traffic light control and route diversion. Research Methodology: The study employed an experimental approach combined with prototyping. Vehicle detection was performed using the YOLO algorithm on CCTV footage collected from congestion-prone areas. The resulting data were processed and visualized through a web-based dashboard. System performance was evaluated based on vehicle detection accuracy and real-time processing speed under various traffic conditions. Results: The developed system successfully detected vehicles from CCTV footage in real-time and displayed traffic density information through an interactive web dashboard. The system enabled adaptive traffic management by providing authorities with accurate and timely data on congestion patterns. Conclusions: The study demonstrates that integrating YOLO-based vehicle detection with a web-based dashboard improves traffic management efficiency in Bandar Lampung City. Real-time monitoring and data visualization enhance the ability of authorities to make informed, timely decisions, contributing to more effective traffic control. Limitations: The study is limited by the use of CCTV footage from selected congestion-prone areas, a relatively small dataset, and potential variability in detection accuracy under extreme weather or low-light conditions. Contribution: This research provides a practical model for real-time traffic monitoring and management using YOLO and web-based visualization. The system offers a replicable framework for other urban areas facing similar traffic congestion challenges and supports data-driven policymaking.

Purpose: This study aims to analyze global research trends in lung cancer detection using a bibliometric approach. It focuses on identifying publication growth, dominant research themes, citation patterns, and collaboration networks to better understand the direction and innovation of lung cancer detection research. Methods: A bibliometric analysis was conducted using publication records retrieved from the Scopus database covering the period from 2019 to 2024. Key indicators such as publication output, citation counts, keyword co-occurrence, and author collaboration networks were analyzed. Results: The results indicate a steady increase in publications related to lung cancer detection over the analyzed period. Major research themes include circulating tumor DNA, early detection strategies, next-generation sequencing, and liquid biopsy technologies. The analysis also reveals strong international collaboration networks, highlighting the global nature of lung cancer research and the collective effort to improve detection technologies. Conclusion: The study concludes that research on lung cancer detection is rapidly expanding, driven by technological advancements and growing interest in non-invasive diagnostic approaches. Emerging technologies are expected to play a critical role in enhancing early diagnosis and reducing lung cancer mortality rates. Limitation: This study is limited by its reliance on a single database (Scopus) and a relatively short time frame, which may not capture all relevant publications or long-term research trends. Contribution: This research provides a comprehensive baseline reference for scholars and practitioners, offering valuable insights into current research directions and supporting future advancements in early lung cancer detection methods.