Prediksi Jumlah Permintaan Semen Menggunakan Jaringan Syaraf Tiruan Backpropagation - Mahendro Agni Giri Pawoko; Imam Cholissodin; Ratih Kartika Dewi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Cement is an important material in the development process. Cement production in Indonesia is quite high compared to the amount of consumption. This condition results in oversupply, a condition where the amount of production is greater than the amount of consumption. This resulted in falling cement prices and warehouse full of cement that has not been sold. This makes the Indonesian Cement Association (ASI) to issue its regulation to temporary stop producting cement. This study aims to predict the amount of cement demand in the next time so that regulations can be issued more quickly, so the factory can adjust its production capacity without having to stop production. Many methods can be used to make predictions, for example is Backpropagation Neural Network which is proven to provide good results in predicting, such as predicting the amount of newspaper demand and sugar production. This research uses Backpropagation Neural Network with network architecture of 6 input neurons, 4 hidden neurons and 1 output neuron. The best parameters used are the learning rate of 0.8, the maximum iteration of 200 and the initial weight interval between -1,4 to 1,4. The MSE best predictive value is 0.049064.