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; Karuna Sindhu Krishna Prasad; I Made Putra Arya Winata; I Wayan Widhiada

doi:10.24843/MITE.2023.v22i01.P18

I Made Esa Darmayasa Adi Putra Teknik Mesin Universitas Udayana
Ilham Fauzi
Karuna Sindhu Krishna Prasad
Karuna Sindhu Krishna Prasad
I Made Putra Arya Winata
I Wayan Widhiada

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Abstrak

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.

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