Parni Handayani
Papers
1
Total Citations
3
H-Index
1
About
Parni Handayani is a researcher whose work sits at the intersection of robotics, computer vision, and disaster response technology. Her most-cited paper, "Confusion Matrix Using Yolo V3-Tiny on Quadruped Robot Based Raspberry PI 3B+," demonstrates a practical application of deep learning for real-world humanitarian challenges. In this work, Handayani integrates the lightweight YOLO V3-Tiny object detection algorithm with a quadruped robot platform, using a Confusion Matrix to evaluate its ability to identify and distinguish between living and deceased individuals in disaster zones. This contribution is particularly significant for search-and-rescue operations, where rapid, accurate detection can save lives. Although her citation count is currently modest, the relevance of her research to natural disaster scenarios—especially in regions prone to earthquakes or floods—positions her work as a foundational step toward more autonomous, intelligent rescue robots. By combining accessible hardware like the Raspberry Pi 3B+ with efficient neural networks, Handayani is helping to democratize advanced robotics for critical applications. Her focus on real-time, low-power object detection in challenging environments marks her as an emerging voice in the field of field robotics and disaster informatics.
Research Focus
Key Achievements
Top Papers
- 1