Open AccessPREDIKSI HARGA EMAS MENGGUNAKAN FEED FORWARD NEURAL NETWORK DENGAN METODE EXTREME LEARNING MACHINE
Author(s) -
Nisa Afida Izati,
Budi Warsito,
Tatik Widiharih
Publication year - 2019
Publication title -
jurnal gaussian : jurnal statistika undip
Language(s) - English
Resource type - Journals
ISSN - 2339-2541
DOI - 10.14710/j.gauss.v8i2.26641
Subject(s) - mean absolute percentage error , artificial neural network , extreme learning machine , computer science , artificial intelligence , inverse , gold standard (test) , process (computing) , machine learning , statistics , mathematics , geometry , operating system
The prediction of gold price aims to find out the gold price in the future on the basis of historical data on gold prices in the past, so it can be used as a consideration by gold investors to investing in gold. Prediction methods that do not require assumptions, one of which is Artificial Neural Networks. In this study, using Artificial Neural Networks, Feed Forward Neural Network with Extreme Learning Machine (ELM). ELM is a non-iterative algorithm so ELM has advantages in process speed. The input weight and bias for this method are determined randomly. After that, to find the final weight using the Moore-Penrose Generalized Inverse calculation on the hidden layer output matrix. The best model selection criteria uses the Mean Absolute Percentage Error (MAPE). This study shows that the results of the training and testing process from the model 1 input neuron and 7 hidden neurons are very good, because it produces MAPE training = 0.6752% and MAPE testing = 0.8065%. Also gives a very good prediction result because it has MAPE = 0.5499% Keywords: gold price, Extreme Learning Machine, MAPE