Brent Crude Oil Price Forecasting using  the Cascade Forward Neural Network

Fatkhurokhman Fauzi; Dewi Ratnasari Wijaya; Tiani Wahyu Utami

doi:10.29207/resti.v7i4.5052

Fatkhurokhman Fauzi Universitas Muhammadiyah Semarang
Dewi Ratnasari Wijaya Universitas Muhammadiyah Semarang
Tiani Wahyu Utami Universitas Muhammadiyah Semarang

DOI: https://doi.org/10.29207/resti.v7i4.5052

Keywords: MAPE, artificial neural networks, cascade forwards, brent, crude oil

Abstract

Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the gross domestic product in 2021. The excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The cascade-forward neural network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the mean absolute percentage error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting the results for Brent crude oil for the next 12 months tends to experience a downward trend until December 2023.

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