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

• Published in: Indonesian Journal of Electrical Engineering and Computer Science
• Main Author: Huzir S.M.H.M.; Mahabob N.Z.; Amidon A.F.M.; Ismail N.; Yusoff Z.M.; Taib M.N.
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136117777&doi=10.11591%2fijeecs.v27.i3.pp1358-1365&partnerID=40&md5=2474c86f0ca86354204c45ea176b2d7f

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. © 2022 Institute of Advanced Engineering and Science. All rights reserved.