Today, crude oil trading industry is still an important industry in the world because it still has high fuel oil consumption. The crude oil prices tend to fluctuate causing the prediction of crude oil in the coming periods to be a challenge. Forecasting the price of crude oil can be done by various methods, one of them is ARIMA Box-Jenkins model with OLS method to estimate the parameter, but this method has several assumptions that must be met. As time goes by, many methods that discovered, one of them is artificial neural network which can combined with various parameter optimization methods such as Adaptive Simulated Annealing algorithm. Adaptive Simulated Annealing algorithm is an optimization method that inspired by the process of crystallization, the advantages of this algorithm has a running time faster than similar algorithms. The combination of artificial neural networks and Adaptive Simulated Annealing algorithms can be used to model the historical data without requiring assumptions in the analysis. Based on the analysis on this research, the best model is obtained FFNN 2-5-1 with MAPE value of 1.0042%. Keywords: neural network, Adaptive Simulated Annealing, crude oil.
Hazardous Source Estimation Using an Artificial Neural Network, Particle Swarm Optimization and a Simulated Annealing Algorithm
