  This paper examines the use of artificial neural network approach in
identifying the origin of clove buds based on metabolites composition.
Generally, large data sets are critical for accurate identification. Machine
learning with large data sets lead to precise identification based on origins.
However, clove buds uses small data sets due to 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 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.
