Agarwood Oil Quality Classifier Using Machine Learning

M. A., Abas and N. S. A., Zubir and N., Ismail and N. A. M., Ali and Mohd Hezri Fazalul, Rahiman and Saiful Nizam, Tajuddin and M. N., Taib (2017) Agarwood Oil Quality Classifier Using Machine Learning. In: International Symposium On Computational Intelligence And Application, Jul 14, 2017 - Jul 15, 2017 , Melaka, Malaysia. . (Unpublished)

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Agarwood Oil is known as one of the most expensive and precious oils being traded. It is widely used in traditional ceremonies, and religious prayers. It’s quality plays an important role on the market price that it can be traded. This paper proposes on a proper classification method of the agarwood oil quality using machine learning model k-nearest neighbour (k-NN). The chemical compounds of the agarwood oil from high and low quality are used to train and build the k-NN classifier model. Correlation-based feature selection was used to reduce the dimension of the data before it is being fed into the model. The results show a very high accuracy (100%) model trained and can be used to classify the agarwood oil quality accurately.

Item Type: Conference or Workshop Item (Speech)
Uncontrolled Keywords: Agarwood oil; K-Nearest Neighbours; Quality; Machine learning
Subjects: Q Science > Q Science (General)
Faculty/Division: Faculty of Industrial Sciences And Technology
Depositing User: Mrs. Neng Sury Sulaiman
Date Deposited: 02 Aug 2017 07:06
Last Modified: 17 Jan 2018 02:55
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