Deteksi Kantuk Melalui Citra Wajah Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM) dan Klasifikasi Support Vector Machine (SVM)

  • Noni Charimmah Teknik Telekomunikasi, Telkom University
  • Ervi Lanovia Teknik Telekomunikasi, Telkom University
  • Koredianto Usman Teknik Telekomunikasi, Telkom University
  • Ledya Novamizanti Teknik Telekomunikasi, Telkom University
Keywords: Kantuk, face detection, Viola-Jones, Gray Level Cooccurrence Matrix, Support Vector Machine

Abstract

Tingginya angka kecelakaan di jalan raya menuntut perkembangan teknologi terkini agar dapat mencegah angka tersebut meningkat. Kecelakaan akibat pengendara yang mengantuk merupakan penyumbang angka kecelakaan tertinggi. Salah satu pencegahan terhadap kecelakaan di jalan raya akibat mengantuk adalah dengan membuat suatu sistem deteksi kantuk melalui pengolahan citra. Sistem tersebut mengolah video yang di rekam untuk mengambil bagian mata dan mulut. Video diambil per-frame dan dilakukan face detection, eye detection, dan mouth detection. Proses tersebut dilakukan dengan menggunakan algoritma Viola-Jones. Setelah diperoleh citra mata dan mulut, dilakukan ekstraksi ciri menggunakan metode Gray Level Co-occurrence Matrix (GLCM). Keluaran dari proses ekstraksi yaitu ciri saat mata dan mulut terbuka atau tertutup. Selanjutnya, klasifikasi keadaan mata dan mulut menggunakan Support Vector Machine (SVM). Sistem akan menghasilkan peringatan ketika pengendara terdeteksi mengantuk.

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Published
2020-03-20
How to Cite
[1]
N. Charimmah, E. Lanovia, K. Usman, and L. Novamizanti, “Deteksi Kantuk Melalui Citra Wajah Menggunakan Metode Gray Level Co-occurrence Matrix (GLCM) dan Klasifikasi Support Vector Machine (SVM)”, SENTER, pp. 174-185, Mar. 2020.
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