Forecasting of palm oil export in Malaysia using : artificial neural network / Nur Ashyda Lukman

Lukman, Nur Ashyda (2019) Forecasting of palm oil export in Malaysia using : artificial neural network / Nur Ashyda Lukman. Degree thesis, Universiti Teknologi Mara Perlis.

Abstract

Palm oil plays a dominant role in the world of vegetables oils export market as now export of palm oil has become one of the biggest export in Malaysia. As the increasing in the number of demand of the palm oil from other countries, the forecasting of the number of the export of palm oil has become an essential. An accurate forecasting of palm oil export is important especially because when dealing with other countries we need to keep up decent image and good name of our country. This study apply Artificial Neural Network (ANN) in forecasting the export of the palm oil in Malaysia. Monthly data from January 2013 to August 2018 that was obtained from Malaysian Palm Oil Board (MPOB) was used in this study. Quick Propagation (QP) algorithm, Conjugate Gradient Descent (GCD) algorithm, Quasi-Newton (QN) algorithm, and Levenberg-Marquardt (LM) algorithm was used in this study. Gradient Descent (GCD) algorithm was concluded as the most suitable algorithm based on the value of error measure. The value of the forecasted also shows an increasing value as it was suitable to used ANN in forecasting the export of palm oil.

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