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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Siti Mariatul Hazwa Mohd Huzir
Universiti Teknologi MARA (UiTM)

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The significance of artificial intelligent technique in classifying various grades of agarwood oil Aqib Fawwaz Mohd Amidon; Siti Mariatul Hazwa Mohd Huzir; Zakiah Mohd Yusoff; Nurlaila Ismail; Mohd Nasir Taib
Indonesian Journal of Electrical Engineering and Computer Science Vol 29, No 1: January 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v29.i1.pp261-269

Abstract

Agarwood oil quality is often separated into two or three categories. This makes classifying agarwood oil quality using current methods difficult. Current approaches rely solely on human perception to determine the quality of agarwood, whether in raw material or oil. This technique has other undesirable implications. It can affect the human sensory system, particularly the eyes and nose. Categorization takes time, which is a considerable expense to succeed in this method. As a result, a new classification system should be devised. The chemical components in agarwood oil are used to classify it in this study. In this study, samples with preprocessing data from two to five quality levels were used. The purpose is to categorize this data based on its qualities and analyze whether this new quality group is acceptable. The K-nearest neighbours (KNN) approach was used to classify all samples and their properties for this dataset. All samples may be correctly classified by grade without any errors. This shows the chemical compound-based classification of agarwood oil can be retained. With these findings, future agarwood oil research may focus on building a new classification.
Co-Authors Aqib Fawwaz Mohd Amidon Mohd Nasir Taib Nurlaila Ismail Zakiah Mohd Yusoff