Implementation of BiLSTM to Predict World Crude Oil Prices

Authors

  • Firda Yunita Sari
  • Nurissaidah Ulinnuha UIN Sunan Ampel Surabaya

Keywords:

BiLSTM, MAPE, Parameters, Prediction, World Crude Oil Prices

Abstract

The main source of energy worldwide is crude oil, which is used by almost all countries as an energy source. Crude oil plays a key role in driving the global economy, especially in the industrial and transportation sectors. Along with technological developments, crude oil price predictions can be made more sophisticated using artificial intelligence-based methods, one of which is the Bidirectional Long Short-Term Memory (BiLSTM) method which is a development of the Long Short-Term Memory (LSTM) method by combining past and future information when processing sequential data, BiLSTM uses forward and backward LSTM simultaneously to increase accuracy. The study used world crude oil price data for 1 year. There are 57 tests with several parameters such as data division, number of neurons, batch size, and activation function. After testing with the BiLSTM method for 57 scenarios, there is the smallest MAPE value of 0.09% at a data division of 90:10, number of neurons 100, batch size of value 4, and ReLu activation function. The resulting prediction model is highly accurate based on the MAPE criterion value.

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Published

2025-05-30

How to Cite

Sari, F. Y., & Ulinnuha, N. (2025). Implementation of BiLSTM to Predict World Crude Oil Prices. KUBIK: Jurnal Publikasi Ilmiah Matematika, 10(1), 34–47. Retrieved from https://journal.uinsgd.ac.id/index.php/kubik/article/view/40385

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Vol. 10 No. 1 (2025): IN PRESS

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