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

1Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.

Abstract

Essential oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. In Malaysia, the recognition system for identifying the essential oil quality still does not reach its standard since mostly graded by using human sensory evaluation. However, previous researchers discovered new modern techniques to present the quality of essential oils by analyse the chemical compounds. Agarwood essential oil had been chosen for the proposed integrated intelligent models with the implementation of k-nearest neighbor (k-NN) due to the high demand and an expensive natural raw world resource. k-NN with Euclidean distance metrics had better performance in terms of its confusion matrix, sensitivity, precision accuracy and specificity. This paper presents an overview of essential oils as well as their previous analysis technique. The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.

Cite

CITATION STYLE

APA

Huzir, S. M. H. M., Mahabob, N. Z., Amidon, A. F. M., Ismail, N., Yusoff, Z. M., & Taib, M. N. (2022). A preliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading. Indonesian Journal of Electrical Engineering and Computer Science, 27(3), 1358–1365. https://doi.org/10.11591/ijeecs.v27.i3.pp1358-1365

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free