Aceng Trisnawan
Papers
1
Total Citations
10
H-Index
1
About
Aceng Trisnawan is a researcher specializing in robotics, control systems, and artificial intelligence, with a particular focus on integrating adaptive neuro-fuzzy inference systems (ANFIS) with vision-based object detection. His most cited work, "Design of 4 Dof Robot ARM Based on Adaptive Neuro-Fuzzy (ANFIS) using Vision in Detecting Color Objects" (2019, 10 citations), presents a significant contribution to intelligent robotic manipulation. In this paper, Trisnawan designed a 4-degree-of-freedom (DOF) robot arm controlled by an Arduino microcontroller, where four servos govern the base, shoulder, hand, and grip movements. The key innovation lies in combining ANFIS with computer vision, enabling the robot to detect and respond to colored objects autonomously. This work demonstrates how fuzzy logic and neural networks can enhance robotic precision and adaptability in real-time environments. With 10 citations, the paper has influenced subsequent research in educational robotics and low-cost automation. Trisnawan’s research bridges theoretical AI methods with practical engineering applications, making his work valuable for students and researchers exploring intelligent robotic systems, embedded control, and sensor-based automation.
Research Focus
Key Achievements
Top Papers
- 1