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All Journal Media Elektrik Jurnal Sipakatau
Mudarris Mudarris
Universitas Negeri Makassar, Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Physics Social Sciences Veterinary Other

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Penerapan Teknologi Tepat Guna Berbasis Energi Surya untuk Pengolahan Hasil Pertanian Menjadi Berbagai Produk Industri Rumah Tangga Muhammad Hasim S., S.,; Iris Sumariyanto; Wahyudi Wahyudi; Akmal Hidayat; Mudarris Mudarris
Jurnal Sipakatau: Inovasi Pengabdian Masyarakat Vol. 1 No. 3 (2024): Jurnal Sipakatau
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61220/jsipakatau.v1i3.248

Abstract

Indonesia as an agricultural country has great potential in processing agricultural products. However, most agricultural products are still processed into raw products with low economic value. Traditional processing of agricultural products into home industry products is inefficient, resulting in a lot of wasted crops. The solution to increase efficiency is to apply appropriate technology based on solar energy. Solar energy, as an environmentally friendly renewable energy source, can reduce the use of fossil fuels and greenhouse gas emissions, while increasing community income. The service shows that the application of appropriate technology for processing agricultural products into various solar energy-based home industry products is more efficient and meets quality standards. The benefits include economic improvement, environmental sustainability, and productivity optimization. The implementation method involves seven stages of community development from awareness to technology maintenance and development.
Deep Learning-Based Electromyography (EMG) Signal Classification for Robotic Hand Control Using Convolutional Neural Networks Mudarris Mudarris; Muhammad Haristo Rahman; Aulia Rahmah; Munzir Munzir
Jurnal Media Elektrik Vol. 23 No. 1 (2025): MEDIA ELEKTRIK
Publisher : Jurusan Pendidikan Teknik Elektro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/metrik.v23i1.10264

Abstract

Electromyography (EMG) is one of the most essential bio signals for developing human–machine interfaces capable of translating muscle activity into motion commands, particularly in prosthetic and assistive robotic systems. However, the nonlinear characteristics of EMG, its susceptibility to noise, and its strong dependence on electrode placement make gesture classification a challenging task. This study aims to classify EMG signals for robotic hand control using a deep learning approach based on Convolutional Neural Networks (CNNs). The dataset consisted of 11,678 samples recorded from eight EMG channels across four hand gestures, preprocessed using a Butterworth filter and normalization prior to training with a lightweight CNN architecture. The model performance was evaluated using accuracy, precision, recall, and F1-score. The proposed model achieved an accuracy of 93%, outperforming Support Vector Machines (SVM), k-nearest neighbors (k-NN), and random forests under identical experimental conditions. The novelty of this study lies in the application of an efficient CNN architecture capable of extracting spatial–temporal features end-to-end from raw EMG signals for real-time robotic control. Despite its promising results, this study is limited to four gesture classes and is sensitive to electrode placement variability. These findings provide a foundational contribution to the development of more responsive, adaptive, and easily deployable prosthetic and robotic control systems.
Co-Authors Akmal Hidayat Aulia Rahmah Iris Sumariyanto Muhammad Haristo Rahman Muhammad Hasim Munzir Munzir Wahyudi Wahyudi