Prediksi Curah Hujan Menggunakan Jaringan Syaraf Tiruan Backpropagation pada Software Matlab - Prasetya, Muhammad Erlangga; Ryansyah, Eddy; Rusfauzi Surya, Muhammad; Umaidah, Yuyun (LPPM Universitas Dinamika Bangsa, 2025)
This study aims to design and implement a rainfall prediction model using the Artificial Neural Network (ANN) approach with the backpropagation learning algorithm on the Matlab platform. Rainfall prediction is an important aspect in agriculture, hydrology, and water resources management, which requires accurate and adaptive methods to seasonal data patterns. In this study, monthly rainfall data for Bogor City for the period 2020-2022 was used as the training and testing dataset. The data was normalized using the sigmoid activation function to improve the network training performance. The network architecture consists of 12 input neurons, 10 hidden neurons, and 1 output neuron. The training results showed an error rate (Mean Squared Error) of 0.00090677 with a regression value of 0.99022, while the test results produced a regression of 0.98837. These findings indicate that the backpropagation method in ANN is able to predict rainfall effectively and accurately. This model can be further developed to predict other weather phenomena.