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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) TEKMAPRO Journal of Industrial Engineering and Management CESS (Journal of Computer Engineering, System and Science) Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization Tech-E JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Kaizen : Management Systems and Industrial Engineering Journal IJID (International Journal on Informatics for Development) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Jurnal Sistem Komputer dan Informatika (JSON) Journal of Advanced in Information and Industrial Technology (JAIIT) Journal of Industrial Engineering and Operation Management (JIEOM) Jurnal Pengabdian Kepada Masyarakat Reputasi: Jurnal Rekayasa Perangkat Lunak Go-Integratif : Jurnal Teknik Sistem dan Industri Prosiding Konferensi Nasional PKM-CSR Jurnal Manajemen NUSANTARA: Jurnal Pengabdian Kepada Masyarakat Limits: Journal of Mathematics and Its Applications
Fauzi, Muhammad Dzulfikar
Institut Teknologi Telkom
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Journal : CESS (Journal of Computer Engineering, System and Science)

 1 
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
CESS (Journal of Computer Engineering, System and Science) Vol 7, No 2 (2022): July 2022
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v7i2.33477

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%.
Co-Authors Abdul Kholik Agfianto Eko Putra Agung Fatwanto Agung Fatwanto Ahmad Wali Satria Bahari Johan Alqis Rausanfita Aprilliyanto, Tofik Arda Erico Yuda Arda Erico Yuda Ardian Yusuf Wicaksono Arifin, Hendra Dea Arip Ramadan Arliyanti Nurdin Arliyanti Nurdin , S.T., M.T Asyhab, Naufal Aulia Faqih Rifa’i Bambang Sugiantoro Bunga Pasha Maulidina Chasandra Puspitasari Desita Nur Rachmaniar Emilia Ramona Faisal Dharma Adhinata Granita Hajar Haq, Azizi Khoirul Hendra Dea Arifin Heni Hapsari Heri Setiawan Ida Ayu Putu Sri Widnyani Islami, Mega Cattleya P.A Kasliono Kasliono Khurin 'ien Mukhoyyaroh Laksono Kurnianggoro Maria Ulfah Siregar Matteo Ibastian Maulana, Rizky Fenaldo Mohammad Hamim Zajuli Al Faroby Mohammad Hamim Zajuli Al Faroby Montolalu, Billy Muhammad Akid Musyafa Muhammad Alfian Jauhari Muhammad Mustakim, Muhammad Muhammad Taufiq Nuruzzaman Musyafa, Muhammad Akid Nia Annisa Ferani Tanjung Nicko Nur Rakhmaddian, Nicko Nur Nisa Isrofi Nur Ghaniaviyanto Ramadhan Oktavianita, Annisa Dwi Pertiwi, Kharisma Monika Dian Qori Emalia Putri Mafidah Rachmadi, Andry Rahmaani, Amalia Ratna Rahmawati, Feri Rizky Fenaldo Maulana Rizky Novera Harnaningrum Shofwatul ‘Uyun Siti Amiroch, Siti son, Sumarsono Sri Lestari Sunu Pinasthika Fajar Suryana Wijaya T. Rasmana , Susijanto Wahyono Wahyono Wahyu Andy Prastyabudi Wahyu Sri Mulyani Yudha Mahardika Putra