There is a growing interest in natural remedies and plant-based solutions, yet accurately identifying plants and understanding their properties remains a significant challenge. Existing mobile apps often lack accuracy or require extensive botanical knowledge, limiting their accessibility and usefulness.
There is a growing interest in natural remedies and plant-based solutions, yet accurately identifying plants and understanding their properties remains a significant challenge. Existing mobile apps often lack accuracy or require extensive botanical knowledge, limiting their accessibility and usefulness. Our project, Herbalyze, aims to revolutionize plant identification and knowledge retrieval. By leveraging advanced machine learning algorithms and extensive botanical databases, Herbalyze provides users with accurate and detailed information about plants, including their medicinal properties, potential benefits, and related diseases. This bridges the gap between plant identification and informed decision-making, empowering users to explore the plant world confidently.
| No | Student ID | Name | Learning Path | University |
|---|---|---|---|---|
| 1 | C291D4KX0543 | Lita Yusdia Fatimah | Cloud Computing | Universitas Pasundan |
| 2 | C547D4NY1350 | Ridwan Ahmad Fauzan | Cloud Computing | UIN Sunan Gunung Djati Bandung |
| 3 | M012D4KY3093 | Abror Muhammad Hazim | Machine Learning | Universitas Telkom |
| 4 | M581D4KX3172 | Nimas Ristiya Rahma | Machine Learning | Universitas Nahdlatul Ulama Yogyakarta |
| 5 | M258D4KX3338 | Azizah Sisilia Sulandari | Machine Learning | Universitas Muhammadiyah Malang |
| 6 | A009D4KY3663 | Erick Yodha Satria | Mobile Development | Universitas Gunadarma |
| 7 | A547D4NY4635 | Dian Saputra | Mobile Development | UIN Sunan Gunung Djati Bandung |
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