Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification
Accidents can be caused by external factors on the road, vehicle conditions, or internal factors such as drowsiness. Drowsiness while driving poses risks to the driver and others. An early detection system is crucial to alert drivers to stop or rest if they show signs of drowsiness. Physical signs o...
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Department of Informatics, UIN Sunan Gunung Djati Bandung
2024-12-01
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| author | Sugeng Hendri Praminiarto |
| author_facet | Sugeng Hendri Praminiarto |
| author_sort | Sugeng |
| collection | DOAJ |
| description | Accidents can be caused by external factors on the road, vehicle conditions, or internal factors such as drowsiness. Drowsiness while driving poses risks to the driver and others. An early detection system is crucial to alert drivers to stop or rest if they show signs of drowsiness. Physical signs of drowsiness include a lethargic facial expression, frequent eye blinking, continuous yawning, or nodding off. A detection system utilizing image processing and machine learning can observe these signs by detecting facial landmarks and analyzing activities such as eye blinking, yawning, and head tilt. This study aims to classify the drowsiness condition based on these three factors. The classification process is conducted using machine learning with the Support Vector Machine (SVM) method to determine whether a person is drowsy or not. The dataset consists of the number of eye blinks, head tilts, and yawns. Conditions are classified into two classes, drowsy and not drowsy. In this study, the SVM classification method can predict drowsiness with an accuracy of up to 77% in the conducted tests. |
| format | Article |
| id | doaj-art-4ffab6e50b194d8983546f6597141561 |
| institution | OA Journals |
| issn | 2528-1682 2527-9165 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Department of Informatics, UIN Sunan Gunung Djati Bandung |
| record_format | Article |
| series | JOIN: Jurnal Online Informatika |
| spelling | doaj-art-4ffab6e50b194d8983546f65971415612025-08-20T02:06:01ZengDepartment of Informatics, UIN Sunan Gunung Djati BandungJOIN: Jurnal Online Informatika2528-16822527-91652024-12-019223824810.15575/join.v9i2.10761077Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) ClassificationSugeng0Hendri Praminiarto1Computer Systems Study Program, Faculty of Engineering and Computer Science, Indonesian Computer University, BandungComputer Systems Study Program, Faculty of Engineering and Computer Science, Indonesian Computer University, BandungAccidents can be caused by external factors on the road, vehicle conditions, or internal factors such as drowsiness. Drowsiness while driving poses risks to the driver and others. An early detection system is crucial to alert drivers to stop or rest if they show signs of drowsiness. Physical signs of drowsiness include a lethargic facial expression, frequent eye blinking, continuous yawning, or nodding off. A detection system utilizing image processing and machine learning can observe these signs by detecting facial landmarks and analyzing activities such as eye blinking, yawning, and head tilt. This study aims to classify the drowsiness condition based on these three factors. The classification process is conducted using machine learning with the Support Vector Machine (SVM) method to determine whether a person is drowsy or not. The dataset consists of the number of eye blinks, head tilts, and yawns. Conditions are classified into two classes, drowsy and not drowsy. In this study, the SVM classification method can predict drowsiness with an accuracy of up to 77% in the conducted tests.https://join.if.uinsgd.ac.id/index.php/join/article/view/1076artificial intelligenceclassificationdrowsiness detectionmachine learningsupport vector machine |
| spellingShingle | Sugeng Hendri Praminiarto Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification JOIN: Jurnal Online Informatika artificial intelligence classification drowsiness detection machine learning support vector machine |
| title | Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification |
| title_full | Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification |
| title_fullStr | Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification |
| title_full_unstemmed | Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification |
| title_short | Detection of Drowsiness in Drivers Using Image Processing and Support Vector Machine (SVM) Classification |
| title_sort | detection of drowsiness in drivers using image processing and support vector machine svm classification |
| topic | artificial intelligence classification drowsiness detection machine learning support vector machine |
| url | https://join.if.uinsgd.ac.id/index.php/join/article/view/1076 |
| work_keys_str_mv | AT sugeng detectionofdrowsinessindriversusingimageprocessingandsupportvectormachinesvmclassification AT hendripraminiarto detectionofdrowsinessindriversusingimageprocessingandsupportvectormachinesvmclassification |