Optimasi Fuzzy Artificial Neural Network dengan Algoritma Genetika untuk Prediksi Harga Crude Palm Oil

Abstract

Crude Palm Oil (CPO) is one of Indonesia's best export commodities. CPO production competition causes price fluctuations so that it can trigger losses. The solution that can be taken to avoid losses is to predict the price of CPO. Time series data in the previous months, starting from January 2009 until January 2020, are used as a reference to predict the next CPO price. In this research, CPO price prediction is carried out with a combination of artificial intelligence concepts, namely Radial Basis Function Neural Network (RBFNN), and fuzzy logic. The combination of these methods, namely Fuzzy Radial Basis Function Neural Network (FRBFNN), is then optimized using genetic algorithms. The prediction results show that the error based on the MAPE value for FRBFNN prediction on training data is 11.7% and the MAPE value for testing data is 9.4%. In the FRBFNN prediction that was optimized using a genetic algorithm, the MAPE value was 10.2% for training data and 8.3% for testing data.

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

“…The GA strings' populations, or chromosomes, are defined according to an optimisation problem. A study employed GA to improve the generalisation ability of the Radial Basis Function Neural Network (RBFNN and Fuzzy Radial Basis Function Neural Network (FRBFNN) [157]. Meanwhile, Ibrahim et al [158] proposed a prediction method of fatty acid and an intelligent nonlinear model to improve the error.…”