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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Science and Technology
Andrivo Rusydi
National University of Singapore
Computer Science & IT Electrical & Electronics Engineering Environmental Science

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Combination of Flex Sensor and Electromyography for Hybrid Control Robot Muhammad Ilhamdi Rusydi; Muhammad Ismail Opera; Andrivo Rusydi; Minoru Sasaki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 5: October 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i5.7028

Abstract

The alternative control methods of robot are very important to solved problems for people with special needs. In this research, a robot arm from the elbow to hand is designed based on human right arm. This robot robot is controlled by human left arm. The positions of flex sensors are studied to recognize the flexion-extension elbow, supination-pronation forearm, flexion-extension wrist and radial-ulnar wrist.The hand of robot has two function grasping and realeasing object. This robot has four joints and six flex sensors are attached to human left arm. Electromyography signals from face muscle contraction are used to classify grasping and releasing hand. The results show that the flex sensor accuracy is 3.54° with standard error is approximately 0.040 V. Seven operators completely tasks to take and release objects at three different locations: perpendicular to the robot, left-front and right-front of the robot. The average times to finish each task are 15.7 ssecond, 17.6 second and 17.1 second. This robot control system works in a real time function. This control method can substitute the right hand function to do taking and releasing object tasks.
Recognition of sign language hand gestures using leap motion sensor based on threshold and ANN models Muhammad Ilhamdi Rusydi; Syafii Syafii; Rizka Hadelina; Elmiyasna Kimin; Agung W. Setiawan; Andrivo Rusydi
Bulletin of Electrical Engineering and Informatics Vol 9, No 2: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (815.536 KB) | DOI: 10.11591/eei.v9i2.1194

Abstract

Hand gesture recognition is a topic that is still investigated by many scientists for numerous useful aspects. This research investigated hand gestures for sign language number zero to nine. The hand gesture recognition was based on finger direction patterns. The finger directions were detected by a Leap Motion Controller. Finger direction pattern modeling was based on two methods: threshold and artificial neural network. Threshold model 1 contained 15 rules based on the range of finger directions on each axis. Threshold model 2 was developed from model 1 based on the behavior of finger movements when the subject performed hand gestures. The ANN model of the system was designed with four neurons at the output layer, 15 neurons at the input layer, seven neurons at the first hidden layer and 5 neurons at the second hidden layer. The artificial neural network used the logsig as the activation function. The result shows that the first threshold model has the lowest accuracy because the rule is too complicated and rigid. The threshold model 2 can improve the threshold model, but it still needs development to reach better accuracy. The ANN model gave the best result among the developed model with 98% accuracy. LMC produces useful biometric data for hand gesture recognition.
Biomass-Based Supercapacitors Electrodes for Electrical Energy Storage Systems Activated Using Chemical Activation Method: A Literature Review and Bibliometric Analysis Ida Hamidah; Ramdhani Ramdhani; Apri Wiyono; Budi Mulyanti; Roer Eka Pawinanto; Lilik Hasanah; Markus Diantoro; Brian Yuliarto; Jumril Yunas; Andrivo Rusydi
Indonesian Journal of Science and Technology Vol 8, No 3 (2023): (ONLINE FIRST) IJOST: December 2023
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v8i3.60688

Abstract

Currently, carbon derived from biomass waste or residues is being intensively utilized as electrodes due to its excellent electrical properties, including high conductivity, appropriate porosity, and a specific surface area suitable for supercapacitor applications. Despite its advantages, the performance of supercapacitors made from biomass-derived carbon is insufficient for engineering applications because of the challenges in obtaining the mesoporous structure of activated carbon (AC). Therefore, this study highlights the potential of biomass-based carbon as the electrodes of a highly efficient supercapacitor, which can facilitate highly efficient current transport in energy storage systems. It comprehensively discusses various biomass material sources and activation methods to produce carbon, with a focus on the physical and electrical properties. Initially, the study discusses carbon activation methods and mechanisms to understand why activating agents and electrolyte solutions have a high specific surface area and specific capacitance. It then concentrates on the chemical activation method and its importance in making AC useful as an efficient electrode. Finally, in this study, various biomass sources were discussed to highlight the performance of supercapacitors electrodes originating from agricultural and wood residues relating to the specific capacitance and capacitance retention. Based on the obtained results, it is concluded that biomass-based carbon materials could be the most advantageous platform material for energy conversion and storage.
Co-Authors Agung W. Setiawan Apri Wiyono Brian Yuliarto Budi Mulyanti Elmiyasna Kimin Ida Hamidah Jumril Yunas Lilik Hasanah Markus Diantoro Minoru Sasaki Muhammad Ismail Opera Ramdhani Ramdhani Rizka Hadelina Roer Eka Pawinanto, Roer Eka Rusydi, Muhammad Ilhamdi Syafii .