Madinatul Munawwaroh

About

Madinatul Munawwaroh is a researcher advancing the frontier of human-robot interaction, with a focus on making robots more responsive and intuitive through speech and visual recognition. Her work centers on integrating machine learning techniques—such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANN)—with robotic control systems. Her most cited paper (39 citations) demonstrates the implementation of speech recognition using Mel-Frequency Cepstrum Coefficients (MFCC) and SVM to command a robot arm for pick-and-place tasks. She also developed the social robot SyPEHUL, capable of real-time face recognition and tracking using Cascade Classification and LBPH, as well as facial expression recognition for more natural human-robot interaction. Her research extends to automated sorting systems, where she applied ANN to enable a 5 DoF robot arm to sort colored objects. With a cumulative citation count exceeding 95, Munawwaroh’s contributions are shaping the next generation of socially aware and autonomously operating robots, bridging the gap between human commands and robotic action.

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
   The design of face recognition and tracking for human-robot interaction

   26 citations · 2017
3. 3
   Control of robot arm based on speech recognition using Mel-Frequency Cepstrum Coefficients (MFCC) and K-Nearest Neighbors (KNN) method

   12 citations · 2017
4. 4
   Colored Object Sorting using 5 DoF Robot Arm based Artificial Neural Network (ANN) Method

   11 citations · 2018
5. 5
   Design of Real Time Facial Tracking and Expression Recognition for Human-Robot Interaction

   7 citations · 2018

Key Collaborators

Contact & Links