| Summary: | - This study presented the optimization of the Multilayer Perceptron (MLP) network with three different training algorithms; Scaled-Conjugate Gradient (SCG), Levenberg Marquardt (LM), and Resilient-Backpropagation (RBP) as one of ongoing research to classify the quality of agarwood oil. The work was done by using MATLAB version 2017a. The training algorithms were applied to agarwood oil data to classify its compounds to the different quality which was high and low. The data collection consists of 96 inputs of the abundances (%) of agarwood oil compounds and the output was the quality of the oil (high=2 and low=1). The process involved in pre- processing data stage; data normalization, data randomization, and data division. The data was divided into three groups with a ratio of 70%, 15%, and 15% for training, validation, and testing respectively. The performance criteria were taken as a consideration which includes confusion matrix, accuracy, sensitivity, specificity, precision, mean square error (mse) and number of epochs. It was found that MLP network architecture with 7 input neurons, 6 hidden neurons and 1 output neuron was suitable for classifying agarwood oil in this study. Levenberg-Marquardt (LM) presented the highest accuracy which was 100% for all training, validation and testing dataset with the lowest MSE. This research is important and contributed as additional research findings especially in the classification of agarwood oil area. © 2023 American Institute of Physics Inc.. All rights reserved.
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