Yudi Irawan Chandra
Papers
4
Total Citations
12
H-Index
2
About
Yudi Irawan Chandra is a researcher whose work spans deep learning optimization, embedded systems, and robotics. His most cited paper, "A comparison study of three single-solution based metaheuristic optimisation for stacked auto encoder" (2019, 5 citations), addresses a critical challenge in deep learning: efficiently training stacked autoencoders. By comparing single-solution metaheuristic algorithms, he offers practical insights for improving layer-wise pre-training and optimization, with applications in audio processing, phonetic recognition, and information retrieval. Beyond deep learning, Chandra applies his technical expertise to practical engineering problems. His 2022 paper on a "Prototype Method for Designing an Automatic Cattle Cage Door Security System Using ATMEGA 328P Microcontroller" (3 citations) demonstrates a low-cost, automated solution for farm management, while his "Cargo Simulation Robot Prototype with Bluetooth Based Motor Driver Shield Using Arduino Uno" (2022, 2 citations) showcases his skill in integrating Android devices with microcontroller-based robotics for object following and cargo transport. Earlier work (2007) on ultrasonic object-following mobile robots further highlights his long-standing interest in autonomous systems. With a total of 12 citations across these four papers, Chandra’s contributions are notable for bridging theoretical optimization and tangible, real-world applications—making his work valuable for students and engineers exploring metaheuristic algorithms, IoT, and robotics.
Research Focus
Key Achievements
Top Papers
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