Peningkatan Akurasi Klasifikasi Awan Cumulonimbus Dari Satelit Himawari-8 Saat Cuaca Ekstrem Dengan Menggunakan Metode Grayscale Thermal Image Dan Neural Network Backpropagation - Triyotomo, Triyotomo; Wulandari, Sari Ayu; Soeleman, M. Arief; Zami , Farrikh Al (Riviera Publishing, 2025)
Cumulonimbus is the only type of cloud that can produce hail, lightning, and thunder. This type of cloud can cause extreme weather that causes damage to public infrastructure and can also cost lives. This research aims to improve Cumulonimbus cloud detection on the Himawari-8 satellite using a combination of the Grayscale Thermal Image method and the method of Artificial Neural Network Backpropagation. The data was taken during the transition season, which is a potential time the onset of extreme weather caused by Cumulonimbus clouds is quite large, and the consequences incurred can cause very significant losses. To detect Cumulonimbus, The Himawari-8 Satellite Image is pre-processed so that an image is obtained gray thermal, then the image is converted into digital data in the form of numbers and from the characterization of the results using histograms. The last process is classified using Artificial Neural Network Propagation. All processes in this study use Matlab to obtain the best classification accuracy. The expected result is an increase in the value of accuracy when using the method of grayscale thermal image compared without using this method. Each accuracy value training data, validating data, and testing data obtained increased from 96.6%, 84.46%, and 80.02 to 100%, 88.9%, and 91.7%.