Heena Jasmine Iwan Putri Dolok Saribu, . (2024) RANCANG BANGUN ALAT PENDETEKSI KANTUK PADA PENGEMUDI MOBIL BERBASIS RASPBERRY PI DAN KAMERA PI DENGAN METODE HISTOGRAM OF ORIENTED GRADIENTS. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.
Text
ABSTRAK.pdf Download (16kB) |
|
Text
AWAL.pdf Download (862kB) |
|
Text
BAB 1.pdf Restricted to Repository UPNVJ Only Download (64kB) |
|
Text
BAB 2.pdf Restricted to Repository UPNVJ Only Download (280kB) |
|
Text
BAB 3.pdf Restricted to Repository UPNVJ Only Download (246kB) |
|
Text
BAB 4.pdf Restricted to Repository UPNVJ Only Download (819kB) |
|
Text
BAB 5.pdf Download (15kB) |
|
Text
DAFTAR PUSTAKA.pdf Download (23kB) |
|
Text
RIWAYAT HIDUP.pdf Restricted to Repository UPNVJ Only Download (21kB) |
|
Text
LAMPIRAN.pdf Restricted to Repository UPNVJ Only Download (3MB) |
|
Text
HASIL PLAGIARISME.pdf Restricted to Repository staff only Download (6MB) |
|
Text
ARTIKEL KI.pdf Restricted to Repository staff only Download (297kB) |
Abstract
In Indonesia, the number of traffic accidents continues to increase every year. Drowsiness is one of the factors that cause traffic accidents. Drowsy conditions are very dangerous when driving because they can cause physical casualties, property losses, and cause congestion. So a tool is made that can detect drowsiness in car drivers during the day and night that can provide warnings to drivers automatically. This tool uses the Histogram of Oriented Gradients (HOG) method and two parameters in detecting drowsiness, namely eyes and mouth. The stages of the process start from taking a face image with a camera pi and processed through the raspberry pi 3 B+ as a controller, then giving output in the form of a warning alarm via a buzzer when the driver is detected to close his eyes and yawn for less than 5 seconds. Based on the results obtained in this tool experiment, this tool successfully detects drowsiness in car drivers when the tool is placed right in front of the driver with a distance of 35-65 cm, then on the right dashboard, center dashboard, and above the speedometer during the day and night with an accuracy value of 100%.
Item Type: | Thesis (Skripsi) |
---|---|
Additional Information: | [No.Panggil: 2010314011] [Pembimbing: Achmad Zuchriadi] [Penguji 1: Silvia Anggraeni] [Penguji 2: Ferdyanto] |
Uncontrolled Keywords: | Drowsiness Detection, Raspberry Pi, Histogram of Oriented Gradients |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Fakultas Teknik > Program Studi Teknik Elektro (S1) |
Depositing User: | Heena Jasmine Iwan Putri Dolok Saribu |
Date Deposited: | 26 Feb 2024 06:52 |
Last Modified: | 26 Feb 2024 06:52 |
URI: | http://repository.upnvj.ac.id/id/eprint/28324 |
Actions (login required)
View Item |