Artificial Neural Network Approach for the Identification of Clove Buds Origin Based on Metabolites Composition

TL;DR

This study applies artificial neural networks to accurately identify the origin of clove buds using metabolite composition, achieving high accuracy even with small datasets, which are typically challenging for machine learning.

Contribution

The paper introduces neural network models tailored for small datasets in clove origin identification, demonstrating high accuracy with backpropagation and resilient propagation methods.

Findings

Backpropagation with one hidden layer achieves over 99% accuracy.

Resilient propagation with two hidden layers achieves nearly 98% accuracy.

Neural networks effectively identify clove origin despite limited data.

Abstract

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.