ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION

This paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origins. However, clove buds uses small data sets due to the lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies the clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively.