Analisis Penerapan Neural Network dalam Memprediksi Produksi Bijih Nikel di Indonesia - Muhammad Edya Rosadi; Dian Agustini; Muthia Farida; Dila Dwi Anjani (Brahmana : Jurnal Penerapan Kecerdasan Buatan, 2022)
Nickel ore is one of the exports from the mining subsector. About 72% of the world's nickel resources are found in lateritic nickel deposits, with approximately 15.8% of these deposits located in Indonesia. Nickel is currently one of the most discussed subjects in the world. As an essential component in the creation of batteries for electric vehicles, nickel is pushing changes in energy consumption. Managing nickel ore output in Indonesia is prudent in light of the government's efforts to increase national development, investment, employment, mining downstream, and export demands. To satisfy domestic and international demand, it is essential to examine nickel ore output. Consequently, an investigation is required to forecast nickel ore production. The dataset utilized is from the Central Bureau of Statistics's Publication of Non-Oil and Gas Mining Statistics for 2017-2020. This study employs a backpropagation network with an artificial neural network. The procedure is carried out by separating training data and testing data to choose the most accurate architectural model, which is subsequently utilized as a predictive model. The architectural models to be utilized with Matlab 6.1 are 2-45-1; 2-60-1; 2-75-80-1; 2-85-1; and 2-100-1. From a series of tests, it was determined that the best architectural model was 2-45-1 with a Mean Square Error of 0.00099549, epoch 335, and an accuracy of one hundred percent. This model was then utilized to create predictions