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

Nirwan Ilyas, Nurtiti Sunusi, Siswanto -, Anisa Kalondeng, Hedi Kuswanto

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.

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Published: 2022-04-18

How to Cite this Article:

Nirwan Ilyas, Nurtiti Sunusi, Siswanto -, Anisa Kalondeng, Hedi Kuswanto, Circulation analysis and forecasting of fuel sales using the backpropagation artificial neural network method, J. Math. Comput. Sci., 12 (2022), Article ID 141

Copyright © 2022 Nirwan Ilyas, Nurtiti Sunusi, Siswanto -, Anisa Kalondeng, Hedi Kuswanto. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

 

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