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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JET (Journal of Electrical Technology) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Otomasi Kontrol dan Instrumentasi Aisyah Journal of Informatics and Electrical Engineering Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Jurnal Teknologi Elekterika Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal for Social Responsibility Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Ilmiah Wahana Pendidikan Jurnal Teknik Elektro Electron: Jurnal Ilmiah Teknik Elektro Power Elektronik : Jurnal Orang Elektro
Latifa, Ulinnuha
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Journal : Brahmana : Jurnal Penerapan Kecerdasan Buatan

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Performa Model YOLOv8 untuk Deteksi Kondisi Mengantuk pada pengendara mobil Armin, Edmund Ucok; Edra, Anggun Purnama; Alifin, Fakhri Ikhwanul; Sadidan, Ikhwanussafa; Sary, Indri Purwita; Latifa, Ulinnuha
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 5, No 1 (2023): Edisi Desember
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v5i1.279

Abstract

Driving while drowsy is identified as a significant risk factor in traffic accidents, yet awareness of this risk is often lower compared to other hazards. Phenomena such as microsleep have been shown to increase the risk of inattention and accidents on the road. This study proposes a novel approach utilizing Deep Learning, specifically YOLOv8, to detect and address the risk of driver drowsiness. To train the model, the researchers employed a secondary dataset consisting of 3708 images, partitioned into 80% for model training and 20% for validation. Multiple models were compared during the training process, and the results indicated that the YOLOv8 model outperformed previous models, achieving a recall value of 0.95261, precision of 0.94655, F1-SCORE of 0.9496, and mAP of 0.98055. This research contributes to the development of more effective drowsiness detection systems using Deep Learning approaches, with promising evaluation results.
Co-Authors ., Apriliana Abdi Bangsa, Insani Afifiansyah, Rafi Alif Ramadhan, Muhammad Amirulhaq Iskandar, Mohammad Armin, Edmund Ucok Aulia Maulana, Dava Azizi, Hilman Berliani, Dhea Cintiya Septa Hasannah Damiri, Adam Darmawan Putra Bahari, Satrya Dessy Agustina Sari Dhimas Ghoza, Satria Dwiputra, Andhika Rifqi Edra, Anggun Purnama Ermanda, Byan Fachri Azizi, Muhammad Fakhri Ikhwanul Alifin Falah Pramanta, Abrar Fazarullah, Muhamad Fazryansah, Muhammad Rizki Hakiim, Azafilmi Husna, Fitria Nurul Ibrahim, Maulana Ikhwanussafa Sadidan, Ikhwanussafa Joko Slamet Saputro KISWORO, DIMAS Kurniadi, Ananda Rizky Lela Nurpulaela Muhammad, Fauzan Nur Akbar Muhammad, Jaysyu Nurdiyanto, Furkhon Padilah, Tesa Nur Pambudi, Agung Priatama Rahmat Hidayat Reni Rahmadewi, Reni Rizky, Alfa Rizmawan, Dilan Sandi Sandi SAPUTRA, YOGI ALDI Sary, Indri Purwita Shafida, Shekar Dwiagung Arifatunnisa Solekha, Ririn Supriyadi, Adimas Prasetyo Sutiyana, Agus Syadidan, Ramlan UTOMO, ANANDA PUTRA Yuliani, Livia Ayudia Yuliarman Saragih Zaki, Maimun