Kernel Comparison on Support Vector Machine for Detecting Stairs Descent

Ahmad Wali Satria Bahari Johan, Ardian Yusuf Wicaksono, Muhammad Dzulfikar Fauzi, Rizky Fenaldo Maulana, Kharisma Monika Dian Pertiwi

Abstract


Terdapat 4 kernel yang dapat digunakan dalam klasifikasi Support Vector Machine dalam membuat hyperplane. Keempat kernel tersebut adalah linear, polynomial, gaussian dan sigmoid. Setiap kernel dapat menghasilkan akurasi yang berbeda-beda. Hal ini dikarenakan pengaruh sebaran data yang diklasifikasikan. Terdapat 2 kelas yang diklasifikasikan, yaitu lantai dan tangga turun. Dilakukan proses ekstraksi fitur tekstur terhadap citra lantai dan tangga turun menggunakan metode Gray Level Co-occurence Matrix. Terdapat 7 fitur dari GLCM yang dihasilkan pada proses ekstraksi fitur. Selanjutnya dilakukan klasifikasi menggunakan Support Vector Machine dengan mencoba setiap kernelnya. Dari hasil pengujian didapatkan kernel linear menghasilkan akurasi yang paling tinggi, yaitu 89%. Kernel sigmoid mendapatkan akurasi 84%. Kernel Gaussian mendapatkan akurasi sebesar 85%. Sedangkan kernel polynomial mendapatkan akurasi yang paling rendah yaitu 78%.


Keywords


Support Vector Machine,; Linear; Polynomial; Gaussian; Sigmoid

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DOI: https://doi.org/10.24114/cess.v7i2.33477

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CESS (Journal of Computer Engineering, System and Science)

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