Klasifikasi Gelombang Otot Lengan Pada Robot Manipulator Menggunakan Support Vector Machine
Abstract
Teknologi robotika semakin berkembang. Banyak orang berinovasi untuk membantu aktivitas mereka, diantaranya membuat robot manipulator untuk mengambil barang di tempat berbahaya atau memindah barang dengan presisi yang sangat tinggi. Pada penelitian ini telah dirancang robot manipulator untuk membantu pasien yang diamputasi pergelangan tangannya agar dapat memegang dan tidak memegang. Sensor Electromyography (EMG) dapat merekam aktivitas listrik yang dihasilkan oleh otot rangka dalam bentuk sinyal yang mempresentasikan gerakan otot. Pada penelitian ini, elektromiogram diekstraksi untuk mendapatkan fitur Root Mean Square (RMS) dan Mean Absolute Value (MAV) kemudian diklasifikasi menggunakan Support Vector Machine (SVM). Metode SVM dipilih karena mampu menemukan hyperplane terbaik sebagai pemisah. Pengendali yang digunakan adalah Arduino yang memerintahkan motor servo untuk menggerakkan robot manipulator sesuai dengan hasil klasifikasi. Penerapan metode Support Vector Machine (SVM) yang bertipe linier memiliki akurasi yang cukup baik dengan keberhasilan 80% pada pengujian dengan subjek yang telah diambil data sampel dan keberhasilan 60% pada pengujian dengan subjek yang tidak diambil data sampel.
Classification of Muscle Wave Arm on Manipulator Robot Using Support Vector Machine
ABSTRACT
Robotics technology is growing. Many people innovate to help their activities, including making manipulator robots to take items in dangerous places or move items with very high precision. In this study a manipulator robot was designed to help patients who amputated their wrists to grip and un-grip. Electromyography (EMG) sensors can record electrical activity produced by skeletal muscles in the form of signals that present muscle movements. In this study, the electromyogram was extracted to get the Root Mean Square (RMS) and Mean Absolute Value (MAV) features then classified using Support Vector Machine (SVM). The SVM method was chosen because it was able to find the best hyperplane as a separator. The controller used is Arduino which instructs the servo motor to move the manipulator robot according to the classification results. The application of the Support Vector Machine (SVM) method which has a linear type has a fairly good accuracy with 80% success in testing with subjects who have taken sample data and 60% success in testing with subjects who are not taken sample data.
Keywords: EMG Sensor, Arm Muscle, MAV, RMS, SVM, Classification.
Keywords
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DOI
https://doi.org/10.21107/rekayasa.v12i2.5406Metrics
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