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All Journal Majalah Ilmiah Teknologi Elektro Majalah Ilmiah Teknologi Elektro
Karuna Sindhu Krishna Prasad
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Evaluasi Model Machine Learning Klasifikasi Gerak Tangan Untuk Sistem Kontrol Prototipe Prostesis Tangan I Made Esa Darmayasa Adi Putra; Ilham Fauzi; Karuna Sindhu Krishna Prasad; I Made Putra Arya Winata; I Wayan Widhiada
Majalah Ilmiah Teknologi Elektro Vol 22 No 1 (2023): (Januari - Juni) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i01.P18

Abstract

Obstacles in the form of loss of function of body parts will cause difficulty in carrying out normal activities. In the application of electromyography (EMG) and electroencephalography (EEG) sensors that are not good at compensating for various kinds of human physical conditions, force sensing resistor (FSR) sensors can be an alternative to EMG and EEG in hand prostheses. In planning the neural network model, the data needed for the actual output is only in the form of hand gestures for post-amputation non-patients. Long Short Term Memory (LSTM) is used because it can handle data processing in the long run which is one of the conditions that arise in sequential data processing. The resulting evaluation metrics are in the form of accuracy values in training data with epoch 200 and accuracy in data testing. The first result with no dropout variation shows the accuracy value in training is 0,9449 and the accuracy in testing is 0,961 with the loss value in training is 0,1284 and the loss in testing is 0,0717. The second result with dropout variations shows the accuracy value in training is 0,9699 and accuracy in testing is 0,9688 with a loss value in training is 0,0803 and loss in testing is 0,1061. the metrics accuracy evaluation generated on the dataset has exceeded the value of 0,9. This indicates that the model has run well for the classification of 11 movements. Keyword — Control System; machine learning; moving arm; prostesis arm.
Optimasi Performansi Sistem Kontrol Prosthesis Lengan Dengan Menggunakan Metode PID Tuning Dengan MATLAB Karuna Sindhu Krishna Prasad; Ilham Fauzi; I.M.E. Darmayasa Adiputra; I.M.P. Arya Winata; I.W. Widhiada
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P02

Abstract

The arm prosthesis in this study will analyze the control system of one finger with 3 joints, the aim is to determine the optimal performance of the control system for the prosthesis using 3 combinations of software, namely autodesk fusion 360, autodesk inventor as the design of the arm prosthesis, and matlab simulink simscape multibody as a simulation of PID tuning with a value of 4.0715 for proportional (P), 99.3021 for integral (I), and 0.039866 for (D) resulting in a movement performance with a rise time of 0.00431 seconds, settling time of 0.026 seconds, and overshoot of 4.76%.
Model Object Detection Neural Network Berbasis Hand Gesture Recognition sebagai Kontrol Prostesis Tangan I Made Esa Darmayasa Adi Putra; I Made Putra Arya Winata; Ilham Fauzi; Karuna Sindhu Krishna Prasad; I Wayan Widhiada
Jurnal Teknologi Elektro Vol 22 No 2 (2023): (Juli - Desember) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i02.P05

Abstract

Most people with disabilities due to accident injuries are patients after an arm amputation which can cause psychological disorders and even major trauma. The urgency of hand prosthesis functionality is increasingly needed. Hand gesture recognition (HGR) can be used to control hand prostheses, judging from the similarity in shape of objects/objects that tend to make the same hand movements. This development uses three common types of movement, namely pinch, pick, and grab. Developing a neural network model capable of implementing this concept is necessary. The neural network model developed uses the YOLOV7 and YOLOV7 tiny pre-trained networks with datasets collected through the public image data scrapping method. The dataset is 317 images and 2278 object labels with a training ratio of 80:20 testing. The training process uses the Pytorch framework with 300 epochs. The results of the loss values for each epoch show that the model is trainable in the given dataset. The training results are then evaluated by evaluating number of parameters, frame per seconds (FPS) and mean average precision (mAP) using a testing dataset. The overall results show the highest evaluation metrics in the model, with YOLOV7 pretrained with parameter number of 36,9 million, FPS of 161, and mAP of 98,11%. The model has the potential to be developed and implemented as a support for the control functionality of hand prostheses.
Evaluasi Model Machine Learning Klasifikasi Gerak Tangan Untuk Sistem Kontrol Prototipe Prostesis Tangan I Made Esa Darmayasa Adi Putra; Ilham Fauzi; Karuna Sindhu Krishna Prasad; I Made Putra Arya Winata; I Wayan Widhiada
Jurnal Teknologi Elektro Vol 22 No 1 (2023): (Januari - Juni) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2023.v22i01.P18

Abstract

Hambatan berupa kehilangan fungsi bagian tubuh akan menyebabkan kesulitan dalam melakukan kegiatan secara normal. Dalam penerapan sensor electromyography (EMG) dan electroencephalography (EEG) yang kurang baik dalam mengimbangi berbagai macam kondisi fisik manusia, sensor force sensing resistor (FSR) dapat menjadi alternatif pengganti EMG dan EEG pada prostesis tangan. Dalam perencanaan model neural network, data yang dibutuhkan pada actual output hanya berupa hand gesture pada orang non pasien pasca amputasi. Long Short Term Memory (LSTM) digunakan karena dapat menangani proses data dalam jangka panjang yang merupakan salah satu keadaan timbul dalam pengolahan data sekuensial.evaluasi metrics yang dihasilkan berupa nilai accuracy pada data training dengan epoch 200 dan accuracy pada data testing. Hasil pertama dengan tanpa variasi dropout menunjukkan nilai accuracy pada training 0,9449 dan accuracy pada testing 0,961 dengan nilai loss pada training 0,1284 dan loss pada testing 0,0717. Hasil kedua dengan variasi dropout menunjukkan nilai accuracy pada training 0,9699 dan accuracy pada testing 0,9688 dengan nilai loss pada training 0,0803 dan loss pada testing 0,1061. evaluasi metrics accuracy yang dihasilkan pada dataset telah melampui nilai 0,9. Hal ini mengindikasikan model telah berjalan dengan baik untuk klasifikasi pada 11 gerakan. Kata Kunci— Sistem kontrol; machine learning; gerakan tangan; prostesis tangan.
Co-Authors I Made Esa Darmayasa Adi Putra I Made Putra Arya Winata I Wayan Widhiada I.M.E. Darmayasa Adiputra I.M.P. Arya Winata I.W. Widhiada Ilham Fauzi