PERANCANGAN SISTEM DETEKSI KANTUK MENGGUNAKAN METODE ARTIFIAL INTELEGEN (AI) BERBASIS NEUROSKY MINDWAVE DAN SMSGATEWAY
| Main Author: | Setiawan, Jaka |
|---|---|
| Format: | Thesis NonPeerReviewed Book |
| Bahasa: | eng |
| Terbitan: |
, 2018
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| Subjects: | |
| Online Access: |
http://eprints.umm.ac.id/40217/1/PENDAHULUAN.pdf http://eprints.umm.ac.id/40217/2/BAB%20I.pdf http://eprints.umm.ac.id/40217/3/BAB%20II.pdf http://eprints.umm.ac.id/40217/4/BAB%20III.pdf http://eprints.umm.ac.id/40217/5/BAB%20IV.pdf http://eprints.umm.ac.id/40217/6/BAB%20V.pdf http://eprints.umm.ac.id/40217/ |
Daftar Isi:
- Sleepiness is a common thing experienced by every human being. But if you feel sleepy when someone is doing a dangerous job, it is certainly a dangerous thing for either the person himself or for other workers. Drowsiness is something that is very much considered in security, especially for drivers who need high concentration where the culprit is required to stay focused in a long period of time. For that we need an application that can give a warning to workers when they are drowsy to rest first. Using a brain wave sensor receiver, it is expected that the application can provide early warning in real-time based on the driver's current condition. The purpose of this final project is to record EEG data from someone and make a sleep detection application as a solution to the problem of accidents to the driver caused by a drowsy factor. The hardware used in this final project is Neurosky Mindwave, which is a commercially available noninvasive EEG reader. This tool has a special function, eSense. Where eSense is a special algorithm of NeuroSky patents in classifying existing brain waves. The wave is Meditation and Attention, which will later be used to detect a person's mental state. The next hardware is raspberry pi3 which is a single-board circuit (SBC) that is the size of a credit card that can be used to run programs, which will then be programmed to classify the waves captured by neurosky so that they can distinguish the conditions experienced by the subject is fuzzy tsukamoto. From the results of data retrieval for approximately 1 hour the author took the fifth minute data so that there were 12 data generated, out of the 12 data there were several subject conditions which were drowsy and not. From the results of sleepiness detection, get the data which will then be classified using fuzzy tsukamoto and get drowsy data with results <50 and data not drowsy with results> 50.