Syahid Al Irfan
Papers
1
Total Citations
5
H-Index
1
About
Syahid Al Irfan is a robotics researcher whose work lies at the intersection of artificial intelligence and humanoid robotics, with a particular focus on enhancing real-time object recognition for autonomous systems. His most cited paper, "Application of Deep Learning Convolution Neural Network Method on KRSBI Humanoid R-SCUAD Robot" (2020), addresses a critical challenge in robotic soccer: enabling humanoid robots to accurately detect and track a ball during dynamic, competitive matches. By applying convolutional neural networks, Al Irfan improved the robot’s visual adaptation and recognition accuracy—a vital step toward more responsive and intelligent autonomous agents. Though early in his career, his work has already garnered attention within the robotics community, contributing to the development of the R-SCUAD platform used in Indonesia’s national robot soccer competition (KRSBI). His research bridges deep learning and mechanical design, offering practical solutions for real-world robotic performance. Al Irfan’s contributions are particularly relevant for students and researchers interested in computer vision, embedded AI, and humanoid locomotion, demonstrating how neural networks can elevate the perceptual capabilities of robots in unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1