Dimas Kurniawan
Papers
1
Total Citations
2
H-Index
1
About
Dimas Kurniawan is a researcher focused on the intersection of assistive robotics and machine learning, with a particular emphasis on developing intelligent systems to improve the quality of life for individuals with physical disabilities. His most cited work, "Comparison of extreme learning machine and neural network method on hand typist robot for quadriplegic person" (2017), explores the critical task of optimizing predictive algorithms for robotic control. In this study, Kurniawan compares the performance of Extreme Learning Machine (ELM) and Backpropagation Neural Network methods, demonstrating that the choice of algorithm directly impacts prediction accuracy—a vital consideration for assistive devices where error can compromise functionality. By systematically evaluating these approaches, his research provides foundational insights into selecting the most effective machine learning techniques for real-time robotic applications. While his citation count is modest, this work represents an important step in bridging computational efficiency with practical, human-centered design. Kurniawan’s contributions underscore the potential of adaptive algorithms to empower quadriplegic individuals, highlighting his commitment to engineering solutions that merge technical rigor with social impact.
Research Focus
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Top Papers
- 1