Implementasi Deep Learning untuk Deteksi Dini Bencana Cuaca Ekstrem Berbasis Analisis Citra Awan - Humuntal Rumapea (Universitas Methodist Indonesia, 2024)
This study aims to implement Deep Learning methods for early detection of extreme weather disasters based on satellite cloud image analysis. The dataset consists of multi-spectral imagery obtained from the Himawari-8 satellite, covering various atmospheric conditions. The proposed approach employs two main models: Convolutional Neural Network as the baseline model and Vision Transformer as the comparative model. The research methodology includes data preprocessing, model training, evaluation using accuracy, precision, recall, and F1-score metrics, and model interpretation using Explainable AI techniques. The results indicate that the Vision Transformer outperforms the CNN model, achieving an accuracy of over 92%. Furthermore, Grad-CAM visualization demonstrates that the model effectively identifies cloud regions associated with extreme weather phenomena. This study contributes to the development of an accurate and interpretable cloud-based early warning system, with potential applications in disaster mitigation, particularly in regions prone to extreme weather such as Indonesia.