Implementasi Root Mean Square Error Untuk Melakukan Prediksi Harga Emas Dengan Menggunakan Algoritma Multilayer Perceptron

Yunita Shara Lubis (Universitas Harapan Medan)
Andi Marwan Elhanafi (Universitas Harapan Medan)
Haida Dafitri (Universitas Harapan Medan)

Prosiding Snastikom
Vol. 1 No. 1 (2021): Prosiding Snastikom 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.