# Artificial Neural Network Backpropagation with Particle Swarm Optimization for Crude Palm Oil Price Prediction
> Salman N.

URL kanonis: https://discover.unhas.ac.id/publications/artificial-neural-network-backpropagation-with-particle-swarm-optimization-for-c
Jurnal / Konferensi: Journal of Physics Conference Series
Tahun terbit: 2018
DOI: https://doi.org/10.1088/1742-6596/1114/1/012088
ISSN: 17426588
Citations: 12

## Authors
- Salman N.

## Abstract
Crude Palm Oil (CPO) is one of the plantation commodities provide the greatest contribution to Indonesia's foreign exchange. Because this plantation is one of the vegetable oil-producing plants with a high economic value. Therefore, the accuracy of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. This study aims to design a method of forecasting the price level for CPO. Neural Network Backpropagation (NN-BP) has been seen as a successful model in many systems recently. In this paper, we will apply Neural Network Backpropagation with a powerful stochastic optimization technique called Particle Swarm Optimization (PSO) to optimize the weight on NN-BP of Crude Palm Oil commodity price. The proposed method is a prediction model using an algorithm which combining particle swarm optimization (PSO) with Neural Network back-propagation (NN-BP) namely PSO-BP. The experimental results show that the proposed PSO–BP algorithm is better than standard Artificial Neural Network Backpropagation for accurate prediction and error convergence by providing better RMSE values.

## Keywords
- Backpropagation
- Particle swarm optimization
- Artificial neural network
- Palm oil
- Computer science
- Convergence (economics)
- Rprop
- Mathematical optimization
- Artificial intelligence
- Machine learning
- Mathematics
- Recurrent neural network
- Types of artificial neural networks
- Economics
- Environmental science
- Economic growth
- Agroforestry

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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