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All Journal Rotasi Mechatronics, Electrical Power, and Vehicular Technology JURNAL TEKNIK MESIN TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Journal of Energy, Mechanical, Material and Manufacturing Engineering Jurnal Teknik Mesin Indonesia
Mochammad Ariyanto
Dosen Jurusan Teknik Mesin, Fakultas Teknik, Universitas Diponegoro Jl. Prof. Sudharto, SH., Tembalang-Semarang 50275, Telp. +62247460059
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Artificial Neural Network Accuracy Optimization Using Transfer Function Methods on Various Human Gait Walking Environments Indrawati, Ragil Tri; Putri, Farika Tono; Safriana, Eni; Isti Nugroho, Wahyu; Prawibowo, Hartanto; Ariyanto, Mochammad
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2159

Abstract

A bionic leg with ergonomic functionality can increase the user’s independence. However, an ergonomic bionic leg can be challenged to be developed. One of its challenges is related to functionality, where the bionic leg motor can be rugged to adapt to the user. One of the solutions for the bionic leg challenge is the application of a motor driver controlled by the user’s muscle signal. EMG signal can be utilized as the user’s signal source. The EMG signal is then fed back into the motor device. EMG signals obtained during a natural walking environment can result in smooth and natural movement. This study classifies EMG signals into 8 classes: a controlled walking environment (treadmill walking with various speeds) and a natural walking environment (ground walking, upstairs and downstairs walking). This research aims to optimize the ANN method using transfer function variations. The best method will be used to train EMG-driven motors for future studies related to bionic legs. The best ANN parameter in this research using Levenberg-Marquardt backpropagation as a training algorithm with transfer function pairing of the exponential function: Hyperbolic tangent sigmoid transfer function and SoftMax transfer function with 98.8% accuracy and 0.036 MSE value. The best method from the experiment and ANN classification can be used as a training method for a bionic leg in future research.
Co-Authors Ahmad Nurmiranto Ananto, Gilar P. Aulia Syukri Aulia, Dito Rahmat Baskoro, Catur Hilman Adritya Haryo Bhakti Dyah Kusuma Dewi Elga P. Yuandi Elta Diah Pamanasari Farika Putri Faturrakhman Khairan Gilar P. Ananto Gunawan Dwi Haryadi Hanny Widura Septriana Indrawati, Ragil Tri Ismoyo Haryanto Isti Nugroho, Wahyu Joga D Setiawan Joga D. Setiawan Joga D. Setiawan Joga Dharma S Joga Dharma Setiawan Joshua W. Syafei Kharisma A. Pambudi Mohamad Irfan Muhamad Rizki Ibrahim Hanan Munadi Munadi Pamanasari, Elta Diah Pambudi, Kharisma A. Parabowo, Teguh Paryanto Pratama, Rakha Rahmadani Prawibowo, Hartanto Purwiantoro, Arman Putri, Farika Putri, Farika Tono Rahmana Muhammad Fajri Rifky Ismail Roni Permana Saputra Safriana, Eni Setiawan, Joga D Sitanggang, Frandy A Sumar Hadi Suryo Syafei, Joshua W. Wahyu Caesarendra Wahyu Caesarendra Wahyu Caesarendra