k-nearest neighbor modelling of agarwood oil samples available in capital of Malaysia market

Erny Haslina Abd Latib, Nurlaila Ismail, Saiful Nizam Tajuddin, Jasmin Jamil, Zakiah Mohd Yusoff

Abstract


Agarwood oil is consumed during traditional ceremonies and even in medicinal purposes due to its effective therapeutic characteristic. As a part of ongoing research on agarwood oil, this paper presented a k-nearest neighbor (k-NN) modelling of agarwood oil samples available in the capital of Malaysia market. The work involved agarwood oil samples from three sources which are lab, local manufacturer and market. The inputs are the chemical compounds and the output is the oil’s resources. The input-output was divided into training and testing dataset with the ratio of 80% to 20%, respectively, before they were fed to the k-NN for model development as well as model validation. During the model development, the k-value was varied from 1 to 5, and their accuracy was observed. The result showed that the k=1 and k=2 shared the similar accuracy for training and testing datasets, which are 98.63% and 100.00%, respectively. This study revealed the capabilities of the k-NN model in classifying the agarwood oil samples to the three sources: lab, local manufacturer and market. It was a significant study and contributed to further work especially those related to agarwood oil research area.

Keywords


agarwood oil; classification; k-nearest neighbor modelling; machine learning; Malaysia markets;

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DOI: http://doi.org/10.11591/ijece.v12i3.pp3158-3165

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578