Extreme weather fluctuations can have a significant impact on the agricultural sector, particularly the rice harvest. This can lead to crop failure and reduced yields, which in turn triggers an increase in the price of staples such as rice.
Increases in the price of staples such as rice can be burdensome for the community, especially for those on low incomes. This can increase poverty and food insecurity, and destabilize the economy.
Accurate weather detection and analysis using machine learning can help predict potential crop failures and fluctuations in rice prices. These predictions can be used to:
- Assist farmers in making decisions: Farmers can use weather prediction information to optimize planting time, select the right rice varieties, and implement cultivation strategies that suit the weather conditions.
- Assist the government in formulating policies**: The government can use weather prediction information to formulate appropriate policies to maintain rice price stability and help farmers affected by crop failure.
- Helping the community prepare: Communities can use weather prediction information to prepare themselves for potential rice price increases and food insecurity.
Y2meta.app-CFarm.-.The.Price.Analysis-.720p.mp4
Link Youtube: Here
| Member | Student ID | Role | Class |
|---|---|---|---|
| Mohammad Ezra Nur Islami | 1217050083 | Machine Learning Developer & Scraper | Distributed System C |
| Muhamad Iqbal Fauzi | 1217050085 | Backend Developer | Distributed System C |
| Muhamad Randi Septiansah | 1217050086 | DevOps | Distributed System C |
- Scraper Data
- Python
- Library pandas, Request, bs4, Matplotlib, SKLearn
- Machine Learning:
- Python
- TensorFlow
- Google Colab
- Backend & DevOps Development:
- Python
- Flask
- Nginx
- Docker Swarm