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

Abstract

<span>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.</span>

A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading

“…The KNN classifier can discover the k most comparable trainings and predict the majority class among them. The advantage of using Euclidean distance in k-NN is the efficiency of its implementation [12], [52]. Existing framework in [43] showed the Euclidean distance as a natural benchmark in access the coefficient of dissimilarity because it relates to everyday physical world's typical notion of distance.…”