Implementasi Root Mean Square Error Untuk Melakukan Prediksi Harga Emas Dengan Menggunakan Algoritma Multilayer Perceptron - Yunita Shara Lubis; Andi Marwan Elhanafi; Haida Dafitri (SEMINAR NASIONAL TEKNOLOGI INFORMASI & KOMUNIKASI, 2021)
Multilayer Perceptron is an artificial neural network that can be used to predict gold prices. This research will produce a combination of parameters, namely the error threshold, learning rate 1 and learning rate 2 which are used in the training data process, which has quite an effect on the resulting error value. From the results of the combination of parameters and testing with the Multilayer perceptron algorithm shown in Figure 4.2, the smallest error value at layer 3 is 54262,375, which is obtained from the learning rate 1 parameter is 0.5, learning rate 2 is 1, learning rate 3 is 1.5. while the largest error value is 46023.9375. The results of the Multilayer Perceptron algorithm in forecasting gold prices can run well, which shows that the model from the implemented training and testing data can predict gold prices.