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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Masyarakat Teknologi Informasi dan Informatika
Edita Rosana Widasari, Edita Rosana
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Public Health Social Sciences

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Journal : journal of information technology and computer science

 1 
Comparative Analysis of Machine Learning Techniques for Hand Movement Prediction Using Electromyographic Signals adani, M. Syakhisk N.; Widasari, Edita Rosana; Setiawan, Eko
Journal of Information Technology and Computer Science Vol. 9 No. 1: April 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202491577

Abstract

The analysis of electromyography (EMG) signals plays a vital role in diverse applications such as medical diagnostics and prosthetic device control. This study focuses on evaluating machine learning methods for EMG signal analysis, specifically in predicting hand movements and controlling prosthetic hands. In contrast to many existing studies that solely employ a limited set of feature extraction methods, we employ a comprehensive comparison technique that encompasses nine machine learning techniques K-Nearest Neighbor (KNN) , State Vector Machine (SVM ) , Decision Tree, Random Forest, Linear Discriminant Analysis (LDA), XGBoost, Naïve Bayes, Gradient Boosting, and Quadratic Discriminant Analysis (QDA)  and five combination of feature extraction methods (Mean Absolute Value (MAV), Root Mean Square (RMS), Waveform Length, Willison Amplitude, and Skewness). The experimental results demonstrate promising accuracy levels, with the best result method being KNN achieving 96.66% accuracy, SVM achieving 95.83% accuracy, and RF achieving 92.5% accuracy. These findings contribute to advancing the understanding of effective machine learning approaches for EMG signal analysis and provide valuable insights for guiding future research in this field. The study also compares the results with previous studies and showcases the effectiveness of the proposed approach.
Volitional Fatigue Monitoring System Using Random Forest With Root Mean Square and Integrated Electromyogram Feature Audrian, Nathaniel; Widasari, Edita Rosana
Journal of Information Technology and Computer Science Vol. 10 No. 1: April 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025101924

Abstract

Athletes often hire personal trainers (PTs) for strength training, where training to volitional fatigue maximizes results but increases injury risk. This study proposes a volitional fatigue monitoring system to assist PTs in preventing fatigue-induced injuries. The system utilizes electromyography (EMG) with features derived from RMS and IEMG signals, with Random Forest classification method. Outputs are displayed on an OLED screen, LED lights, and a website via the WebSocket Protocol. EMG signal disturbances were mitigated with a filter, a battery, and a sport band. Testing involved five subjects aged 20-22 with various arm strength and no exercise background. The test results show that the EMG sensor acquires data within the appropriate range. The system achieved a 91.43% accuracy in muscle fatigue detection and a 1.0284 second average computation time, and produced the expected outputs with 100% accuracy. Therefore, the proposed monitoring system is feasible and reliable for volitional fatigue monitoring.
Enhanced Hand Gesture Classification for a Myoelectric Prosthetic Hand Using Compact CNN with Wearable EMG Sensors Ristanti, Dini Eka; Widasari, Edita Rosana
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102927

Abstract

In Indonesia, many people experience upper-limb functional loss, which limits their ability to perform daily activities. Myoelectric prosthetic hands offer a promising solution for transradial amputees by using electromyography (EMG) signals to control hand movements. However, conventional wired systems may introduce noise that affects EMG signal quality and gesture recognition performance. Therefore, this study proposes an enhanced hand gesture classification approach for myoelectric prosthetic hands using wearable EMG sensors, Savitzky-Golay (SG) filtering, and a compact convolutional neural network (CNN). The dataset consists of EMG signals acquired from eight subjects using an eight-channel wearable EMG sensor, covering four hand gestures: rock, paper, scissors, and OK. The SG filter is applied to smooth the EMG signals and reduce noise while preserving important signal characteristics. Since filtering alone is insufficient to distinguish all gesture types, a compact CNN model is employed for classification. The proposed model achieved an accuracy of 95.15%, precision of 95.16%, recall of 95.15%, and F1-score of 95.15%, demonstrating its effectiveness for EMG-based hand gesture classification in myoelectric prosthetic hand applications.
Co-Authors adani, M. Syakhisk N. Agung Setia Budi, Agung Setia Aji, Eko Prastowo Akbar, Rizky Alhamid, Faticha Kamila Aryawidyawan, Mochammad Farrel Attallah, Abel Putra Audrian, Nathaniel Azizy, Faqih Barlian Henryranu Prasetio Bhanu Shidqianto, Naufal Butarbutar, Erlinda Chatarina Umbul Wahyuni Daffa, Ali Zhafran Dahnial Syauqy Dananjaya, Andreas Wirawan Daniswara, Muhammad Ananta Dewa, Muklas Isa Eko Setiawan Fabiana, Ryzaldi Ananda Faiqoh, Rifdah Fauzi, Muhammad Aqil Fuad, Alifa Azwadina Gabriel Hizkia Marhata Simbolon Ghifari, Achmad Giffary, Muhammad Zufar Hakim, Muhammad Ilham Hanafi, Muhammad Imam Hanifa Maulani Ramadhan Hurriyatul Fitriyah, Hurriyatul IKA NURFARIDA J'Verron, Shadam Ksatria, Fatih Raka Kurnia, Yudisthira Dwi Manurung, Manfredy Patarida Hasiholan Muhammad Fahrizal, Muhammad Musharrif, Mohammad Faiz Nashrullah, Ikbar Razan Nugraha, Aldia Agri Palwa Luktagara, Abim Renara Perkasa, Septiyo Budi Prakoso, Raihan Prakoso, Raihan Adi Pramana, Danish Gyan Prasetio, Barlian Prasetyo, Ragil Hadi Rakhmadhany Primananda, Rakhmadhany Ratanasari, Dittha Rava Athalla, Muhammad Rianta, Razzan Naufal Ristanti, Dini Eka Rizal Maulana, Rizal Rohman, Zahra Auliaul Rohmandzoni, Achmad Santoso, Teguh Surya Darma Saputra, Naufal Rijaldi Shabiyya, Syifa’ Hukma Simbolon, Gabriel Hizkia Marhata Surya Darma Santoso, Teguh Syahrul Chilmi, Syahrul syauqi Toar, Mikhael Ryan Trido, Pradytia Utaminingrum, Fitri Vebrianto, Yudaneru Wijaya Kurniawan Yunus, Ahmad Haykal