Penerapan Algoritma Multiclass Ensemble Support Vector Machine dengan Fungsi Kernel untuk Klasifikasi Human Activity
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Human Activity Recognition adalah teknologi yang memperkenalkan gerakan tubuh manusia menggunakan accelerometer, giroskop, global positioning system, dan kamera. Awal munculnya metode support vector machine digunakan untuk mengklasifikasi 2 kelas, sehingga diperlukan pengembangan untuk mengatasi permasalahan multikelas dan banyaknya dataset yang berskala besar mengakibatkan kinerja menjadi tidak optimal. Tujuan kertas ini adalah menerapkan metode ensemble Support Vector Machine dalam mengklasifikasikan gerakan berjalan, berlari, dan naik tangga berdasarkan sensor accelerometer dan gyroscope pada smartphone. Serta melihat kinerja metode ensemble Support Vector Machine ketika menggunakan kernel linear dan RBF. Hasil akurasi Support Vector Machine kernel linear sebesar 79.66% dan mengalami peningkatan sebesar 88.01% setelah menggunakan ensemble. Sedangkan akurasi untuk Support Vector Machine kernel RBF sebesar 79.51 dan mengalami peningkatan sebesar 88.04% setelah menggunakan ensemble.
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