The significance of artificial intelligent technique in classifying various grades of agarwood oil
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 undesir...
Published in: | Indonesian Journal of Electrical Engineering and Computer Science |
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Institute of Advanced Engineering and Science
2023
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2-s2.0-85141886701 Mohd Amidon A.F.; Huzir S.M.H.M.; Yusoff Z.M.; Ismail N.; Taib M.N. The significance of artificial intelligent technique in classifying various grades of agarwood oil 2023 Indonesian Journal of Electrical Engineering and Computer Science 29 1 10.11591/ijeecs.v29.i1.pp261-269 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141886701&doi=10.11591%2fijeecs.v29.i1.pp261-269&partnerID=40&md5=9a5a431c51bab0f49a853fdcd9ea7de3 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. © 2023 Institute of Advanced Engineering and Science. All rights reserved. Institute of Advanced Engineering and Science 25024752 English Article All Open Access; Gold Open Access |
author |
Mohd Amidon A.F.; Huzir S.M.H.M.; Yusoff Z.M.; Ismail N.; Taib M.N. |
spellingShingle |
Mohd Amidon A.F.; Huzir S.M.H.M.; Yusoff Z.M.; Ismail N.; Taib M.N. The significance of artificial intelligent technique in classifying various grades of agarwood oil |
author_facet |
Mohd Amidon A.F.; Huzir S.M.H.M.; Yusoff Z.M.; Ismail N.; Taib M.N. |
author_sort |
Mohd Amidon A.F.; Huzir S.M.H.M.; Yusoff Z.M.; Ismail N.; Taib M.N. |
title |
The significance of artificial intelligent technique in classifying various grades of agarwood oil |
title_short |
The significance of artificial intelligent technique in classifying various grades of agarwood oil |
title_full |
The significance of artificial intelligent technique in classifying various grades of agarwood oil |
title_fullStr |
The significance of artificial intelligent technique in classifying various grades of agarwood oil |
title_full_unstemmed |
The significance of artificial intelligent technique in classifying various grades of agarwood oil |
title_sort |
The significance of artificial intelligent technique in classifying various grades of agarwood oil |
publishDate |
2023 |
container_title |
Indonesian Journal of Electrical Engineering and Computer Science |
container_volume |
29 |
container_issue |
1 |
doi_str_mv |
10.11591/ijeecs.v29.i1.pp261-269 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141886701&doi=10.11591%2fijeecs.v29.i1.pp261-269&partnerID=40&md5=9a5a431c51bab0f49a853fdcd9ea7de3 |
description |
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. © 2023 Institute of Advanced Engineering and Science. All rights reserved. |
publisher |
Institute of Advanced Engineering and Science |
issn |
25024752 |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1809678023962656768 |