Penerapan Model Gated Recurrent Unit Pada Masa Pandemi Covid-19 Dalam Melakukan Prediksi Harga Emas Dengan Menggunakan Model Pengukuran Mean Square Error - Ilham Alkahfi; Kalvin Chiuloto (Universitas Harapan Medan, 2021)
Gold is a type of precious metal that has economic value and is also profitable because it can be used as an investment tool. Another advantage of investing in gold is that gold has properties that are resistant to inflation. The problem in this study is that the price of gold can fluctuate from day to day so gold is needed to forecast a relatively accurate gold price so that investors are able to get a profit according to the plans that have been made. The use of deep learning techniques makes it very possible to carry out learning using more complex layers in order to receive high accuracy for forecasting gold prices. The research method used in completing this final project is to use the waterfalld method where each work will be carried out sequentially from top to bottom. Forecasting gold prices is useful for the community, because it makes it easier for people to see the price of gold market value in the next few months. In this study, we will use the GRU (Gated Recurrent Unit) algorithm in forecasting the gold price and then use the error rate measurement technique, namely MSE (Mean Square Error) which serves to check the estimated error value in gold price forecasting. What will happen in this research is a system that can predict gold prices every day based on gold price input data. This study uses the Python programming language and uses the GRU (Gated Recurrent Unit) algorithm.