Impact of Feature Selection on XGBoost Model with VGG16 Feature Extraction for Carbon Stock Estimation Using GEE and Drone Imagery - Dalam bentuk pengganti sidang - Artikel Jurnal
I MADE DARMA CAHYA ADYATMA
Informasi Umum
Kode
25.04.371
Klasifikasi
000 - General Works
Jenis
Karya Ilmiah - Skripsi (S1) - Reference
Subjek
Machine Learning
Dilihat
92 kali
Informasi Lainnya
Abstraksi
Carbon stocks are critical to climate change mitigation by capturing atmospheric carbon and storing it in biomass. However, carbon stock estimation faces challenges due to data complexity and the need for efficient analytical methods. This study introduces a carbon stock estimation method that integrates the XGBoost algorithm with VGG16 feature extraction and feature selection techniques to analyze GEE and Drone image datasets. The model is evaluated through four scenarios: without feature selection, using Information Gain, using Feature Importance, and using Recursive Feature Elimination. These scenarios aim to compare feature selection methods to identify the best one for processing complex environmental data. The experimental results show that RFE significantly outperforms other methods, achieving an average RMSE of 6651.62, MAE of 2297.57, and R² of 0.7673. These findings underscore the importance of feature selection in optimizing model performance, particularly for high-dimensional environmental da
- CCH4D4 - TUGAS AKHIR
Koleksi & Sirkulasi
Tersedia 1 dari total 1 Koleksi
Anda harus log in untuk mengakses flippingbook
Pengarang
| Nama | I MADE DARMA CAHYA ADYATMA |
| Jenis | Perorangan |
| Penyunting | Erwin Budi Setiawan |
| Penerjemah |
Penerbit
| Nama | Universitas Telkom, S1 Informatika |
| Kota | Bandung |
| Tahun | 2025 |
Sirkulasi
| Harga sewa | IDR 0,00 |
| Denda harian | IDR 0,00 |
| Jenis | Non-Sirkulasi |
