PROTOTYPE DETEKSI LEVEL KANTUK BERDASARKAN EYE ASPECT RATIO MENGGUNAKAN METODE FACIAL LANDMARK BERBASIS ORANGE Pi

Andri Nofiar Am(1),


(1) Politeknik Kampar
Corresponding Author

Abstract


The Prototype for Detecting Drowsiness Levels based on Eye Aspect Ratio using the Orange Pi-based Facial Landmark Method is a tool that can detect users in three levels of drowsiness, namely normal, drowsiness, or asleep. This tool uses EAR threshold parameters and utilizes the facial landmark method to detect points on the eye. The test results showed that the prototype device was able to function well after undergoing tests on distance, camera angle, eye size, use of glasses and light intensity. At testing distances of 25 cm, 35 cm, 50 cm, 70 cm, 85 cm, 100 cm, 130 cm and 155 cm, the tool can detect the user's eyes according to conditions. However, in tests based on angles it was found that the tool could detect the user's eyes at angles of 90o, 80o and 70o, except at the 60o angle due to the limited viewing angle of the camera so the tool could not detect the condition of the user's eyes at that angle.

References


Fauzan, H. A., & Kurniawan, A. (2022). Aplikasi Warning Alert Pendeteksi Kelelahan Ekspresi Wajah Pada Pengemudi Secara Real-Time Menggunakan Metode You Only Look Once Berbasis Website. 14, 1–13.

Hariesugama, F., Bimantoro, F., & Nugraha, G. S. (2022). Pengenalan Wajah Dan Deteksi Kantuk Menggunakan Metode Haar Cascade Dan Convolutional Neural Network.

Saputra, C. Aj., Erwanto, D., & Rahayu, P. N. (2021). Deteksi Kantuk Pengendara Roda Empat Menggunakan Haar Cascade Classifier Dan Convolutional Neural Network. JEECOM Journal of Electrical Engineering and Computer, 3(1), 1–7. https://doi.org/10.33650/jeecom.v3i1.1510

Maslikah, S., Alfita, R., & Ibadillah, A. F. (2020). Sistem Deteksi Kantuk Pada Pengendara Roda Empat Menggunakan Eye Blink Detection. Jurnal FORTECH, 1, 33–38.

Udayana, I. P. A. E. D., Kherismawati, N. P. E., & Sudipa, I. G. I. (2022). Detection of Student Drowsiness Using Ensemble Regression Trees in Online Learning During a COVID-19 Pandemic. Telematika, 19(2), 229. https://doi.org/10.31315/telematika.v19i2.7044.

Jadhavar, P. P., Barahate, P., Chaudhari, S., Keskar, G., & Nene, A. (2023). Driver Drowsiness Detection system using opencv and keras. International Journal of Progressive Research in Engineering Management and Science, 3, 482–487. https://doi.org/10.58257/ijprems31401

Bachtiar, F. A., & Wafi, M. (2021). Komparasi Metode Klasifikasi untuk Deteksi Ekspresi Wajah Dengan Fitur Facial Landmark. Jurnal Teknologi Informasi dan Ilmu Komputer, 8(5), 949–956. https://doi.org/10.25126/jtiik.2021834434.

Setiady, W., & Setiawan, A. D. A. (2022). Rancang Bangun Orange Pi 3 Lts Sebagai Server Untuk Tablet Pendant Dengan Menggunakan Node-Red. Jurnal Inkofar, 6(2), 134–140. https://doi.org/10.46846/jurnalinkofar.v6i2.236.

Azrin, U., Ziad, I., & Suroso, S. (2022). Rancang Bangun Smart Box Penerima Paket Berbasis IoT Menggunakan Raspberry Pi. Emitor: Jurnal Teknik Elektro, 22(2), 118–125. https://doi.org/10.23917/emitor.v22i2.19405.

Wijaya, T., Salim, A., & Pusparini, N. N. (2023). Perancangan Automatic Tempat Sampah Pada Sistem Arduino Uno R3. Jurnal Ilmiah Informatika, 11(02), 113–120. https://doi.org/10.33884/jif.v11i02.7377

Wijaya, D. C. M., & Khariono, H. (2022). Pemantauan Ph Berbasis Nodemcu32 Terintegrasi Bot Telegram Melalui Platform I-Ot.Net. Jurnal Informatika Polinema, 8(3), 53–62. https://doi.org/10.33795/jip.v8i3.868

Putra, A. A., Susanto, E., & Prihatiningrum, N. (2021). Sistem Perekam Kecepatan Sepeda Motor Saat Kecelakaan Menggunakan Microsd. 8(6), 11479–11484.

Anton, M., Sulistiyanto, & Basri, M. H. (2020). Perancangan Jam Istiwa Otomatis Menggunakan Running Text dan Speaker Sebagai Alat Bantu Waktu Sholat Di Masjid Nurul Hidayah Al-Taqwa. 5(2), 43–48.

Sejati, R. H. P., & Mardhiyyah, R. (2021). Deteksi Wajah Berbasis Facial Landmark Menggunakan OpenCV dan Dlib. 5(2), 144–148.

Aqsha Ramadhana Lubis, A., Indah Purnama, S., & Aly Afandi, M. (2023). Sistem Pendeteksi Kantuk Berbasis Metode Haar Cascade Untuk Aplikasi Computer Vision. Techno.COM, 22(3), 589–598.


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DOI: 10.58486/jsr.v8i1.358

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