Circulation analysis and forecasting of fuel sales using the backpropagation artificial neural network method

Abstract

The availability of a general fuel supply for the community is an interesting matter to study. This is because fuel is a basic need for the community. This paper aims to model and predict the general fuel demand and circulation using the Backpropagation Artificial Neural Network (ANN) method. A neural network consists of a set of numbers in simple processing elements called neurons, units, cells, or nodes. Each neuron is connected to the other neurons in a manner directed by communication links and by interrelated weights. Weights are represented as information that will be used by the network to solve a problem. In this study, secondary data is used on the volume of fossil fuel sold daily at the Hasanuddin gas station in Makassar from January 1, 2018 – March 29, 2021, with a lot of data 1121 days. The types of fossil fuels studied are Premium and Pertalite. The results obtained indicate that the best model obtained for the Pertalite ANN architecture with 4 inputs and 5 neurons in the hidden layer has the best accuracy with a MAPE of 17.64% which is classified as good, while the Pertalite ANN architecture with 7 inputs and 25 neurons in the hidden layer has accuracy. the best with a MAPE of 14.64% which is classified as good.