M. Yusuf Solih Nurasyidiek

About

M. Yusuf Solih Nurasyidiek is a researcher specializing in robotics, speech recognition, and intelligent control systems, with a focus on bridging machine learning techniques with practical robotic applications. His work centers on developing innovative human-robot interaction systems, particularly through voice-controlled robotic arms and automated sorting mechanisms. Nurasyidiek's most impactful contribution is his 2018 study on speech recognition-based robot arm control, combining Mel-Frequency Cepstrum Coefficients (MFCC) for feature extraction with Support Vector Machine (SVM) classification — a paper that has garnered 39 citations and established him as a notable voice in human-robot interaction research. Building on this, his earlier 2017 work explored the same MFCC framework paired with the K-Nearest Neighbors (KNN) algorithm to control a 5-DoF Arduino-based robot arm, earning 12 citations. His 2018 research on colored object sorting using Artificial Neural Networks (ANN) further demonstrates his versatility in applying deep learning to real-time industrial automation tasks, accumulating 11 citations. Collectively, with over 60 citations across three key publications, Nurasyidiek's research makes meaningful contributions to accessible, intelligent robotics, offering practical solutions for automation and human-machine communication in industrial and educational settings.

Research Focus

Key Achievements

Top Papers

1. 1
   The Implementation of Speech Recognition using Mel-Frequency Cepstrum Coefficients (MFCC) and Support Vector Machine (SVM) method based on Python to Control Robot Arm

   39 citations · 2018
2. 2
   Control of robot arm based on speech recognition using Mel-Frequency Cepstrum Coefficients (MFCC) and K-Nearest Neighbors (KNN) method

   12 citations · 2017
3. 3
   Colored Object Sorting using 5 DoF Robot Arm based Artificial Neural Network (ANN) Method

   11 citations · 2018

Key Collaborators

Contact & Links