Sustainable Living
Team ID: CH2-PS276
Hi everyone! We are from team C23-PS185. We consist of 7 people and these are our team members:
| Name | Bangkit ID | Learning Path |
|---|---|---|
| Ahmad Zaki Firdaus | M008BSY0038 | Machine Learning |
| Samuel Budi Satrio | M008BSY0315 | Machine Learning |
| Vincent Yeozekiel | M008BSY1786 | Machine Learning |
| Angga Restu Aji | C391BSY3460 | Cloud Computing |
| Hizkia Pratama | C391BSY3901 | Cloud Computing |
| Bintang Nasution | A159BSY2178 | Mobile Development |
| Aldi Perdana Asri | A438BKY4487 | Mobile Development |
Executive Summary/Abstract: In recent years, driving safety has become a global issue that demands serious attention. Globally, the World Health Organization (WHO) reports that traffic accidents are the leading cause of death among adolescents and young adults. In Indonesia, this reality has become even more urgent with a significant upward trend in accidents. Data from the Central Bureau of Statistics recorded a spike in accident cases from 100,028 cases in 2020 to 103,645 cases the following year, and a drastic jump to 131,500 cases in 2022. This shows the urgency of improving road safety as a national priority. How can we reduce traffic accidents caused by drowsiness by applying machine learning? StayAwake is one such solution, an Android-based app designed to monitor drivers in real-time while driving. It uses Convolutional Neural Networks and Computer Vision methods to identify signs of inattention or abnormal conditions in the driver's body, such as fatigue or drowsiness. Upon detecting such indications, StayAwake will activate an alarm to increase the driver's alertness. Thus, this application plays an active role in preventing traffic accidents that can be caused by drowsiness while driving.
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Tools
- Figma: tools to design assets, user interface, and wireframing
- GitHub: tools for version control system
- Postman: tools for testing our API
- Google Colab: Creating a detection model
- Anaconda: Creating a detection model
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IDE
- Visual Studio Code: for creating API
- Android Studio: for creating mobile app
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Library
- Machine Learning: Numpy, Shutil, OS, Matplotlib, Pandas, TensorFlow, CV2, Keras, Scikit-learn
- Mobile Development: Android KTX, Retrofit, Okhttp, Gson, Glide, CameraX, Parcelize, Coroutines, Live Data, View Model, Room, Fragment.
- Cloud Computing: NodeJs, Hapi
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Platform
- Google Cloud Platform: Platform to deploy API
- Mongodb: Platform to create database
- Kaggle, RoboFlow: Searching Dataset
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API
- TensorFlow Lite: Mobile library for deploying models on mobile
In our project is divided into 3 branches:
The app can be downloaded here: StayAwake
