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The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification

Agarwood oil is well known for its unique scent and has many usages; as an incense, as ingredient in perfume, is burnt during religious ceremonies and is used in traditional medical preparation. Therefore, agarwood oil has high demand and is traded at different price based on its quality. Basically,...

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Main Authors: Noor Syafina, Mahamad Jainalabidin, Aqib Fawwaz, Mohd Amidon, Nurlaila F., Ismail, Zakiah, Mohd Yusoff, Saiful Nizam, Tajuddin, Mohd Nasir, Taib
Format: Article
Language:English
Published: Intelektual Pustaka Media Utama 2022
Subjects:
HD28 Management. Industrial Management
Q Science (General)
T Technology (General)
Online Access:http://umpir.ump.edu.my/id/eprint/40219/1/The%20k-nearest%20neighbor%20modelling%20by%20varying%20Mahalanobis.pdf
http://umpir.ump.edu.my/id/eprint/40219/
https://doi.org/10.11591/ijaas.v11.i3.pp242-252
https://doi.org/10.11591/ijaas.v11.i3.pp242-252
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spelling my.ump.umpir.402192024-02-09T07:47:56Z http://umpir.ump.edu.my/id/eprint/40219/ The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification Noor Syafina, Mahamad Jainalabidin Aqib Fawwaz, Mohd Amidon Nurlaila F., Ismail Zakiah, Mohd Yusoff Saiful Nizam, Tajuddin Mohd Nasir, Taib HD28 Management. Industrial Management Q Science (General) T Technology (General) Agarwood oil is well known for its unique scent and has many usages; as an incense, as ingredient in perfume, is burnt during religious ceremonies and is used in traditional medical preparation. Therefore, agarwood oil has high demand and is traded at different price based on its quality. Basically, the oil quality is classified by using physical properties (odor and color) and this technique has several problems: not consistent in term of accuracy. Thus, this study presented a new technique to classify the quality of agarwood oil based on chemical properties. The work focused on the k-nearest neighbor (k-NN) modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification. It involved of 96 samples of agarwood oil, data pre-processing (data randomization, data normalization, and data division to testing and training datasets) and the development of k-NN model. The training dataset is used to train the k-NN model, and the testing dataset is used to test the developed model. During the model development, Mahalanobis and correlation are varied in k-NN distance metric. The k-NN values are ranging from 1 to 10. Several performance criteria including resubstitution error (closs), cross-validation error (kloss) and accuracy were applied to measure the performance of the built k-NN model. All the analytical work was performed via MATLAB software version R2020a. The result showed that the accuracy of Mahalanobis distance metric has a better performance compared to correlation from k = 1 to k = 5 with the value of 100.00%. This finding is important as it proved the capabilities of k-NN modelling in classifying the agarwood oil quality. Not limited to that, it also contributed to the agarwood oil research area as well as its industry. Intelektual Pustaka Media Utama 2022-09 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/40219/1/The%20k-nearest%20neighbor%20modelling%20by%20varying%20Mahalanobis.pdf Noor Syafina, Mahamad Jainalabidin and Aqib Fawwaz, Mohd Amidon and Nurlaila F., Ismail and Zakiah, Mohd Yusoff and Saiful Nizam, Tajuddin and Mohd Nasir, Taib (2022) The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification. International Journal of Advances in Applied Sciences, 11 (3). pp. 242-252. ISSN 2252-8814. (Published) https://doi.org/10.11591/ijaas.v11.i3.pp242-252 https://doi.org/10.11591/ijaas.v11.i3.pp242-252
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic HD28 Management. Industrial Management
Q Science (General)
T Technology (General)
spellingShingle HD28 Management. Industrial Management
Q Science (General)
T Technology (General)
Noor Syafina, Mahamad Jainalabidin
Aqib Fawwaz, Mohd Amidon
Nurlaila F., Ismail
Zakiah, Mohd Yusoff
Saiful Nizam, Tajuddin
Mohd Nasir, Taib
The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification
description Agarwood oil is well known for its unique scent and has many usages; as an incense, as ingredient in perfume, is burnt during religious ceremonies and is used in traditional medical preparation. Therefore, agarwood oil has high demand and is traded at different price based on its quality. Basically, the oil quality is classified by using physical properties (odor and color) and this technique has several problems: not consistent in term of accuracy. Thus, this study presented a new technique to classify the quality of agarwood oil based on chemical properties. The work focused on the k-nearest neighbor (k-NN) modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification. It involved of 96 samples of agarwood oil, data pre-processing (data randomization, data normalization, and data division to testing and training datasets) and the development of k-NN model. The training dataset is used to train the k-NN model, and the testing dataset is used to test the developed model. During the model development, Mahalanobis and correlation are varied in k-NN distance metric. The k-NN values are ranging from 1 to 10. Several performance criteria including resubstitution error (closs), cross-validation error (kloss) and accuracy were applied to measure the performance of the built k-NN model. All the analytical work was performed via MATLAB software version R2020a. The result showed that the accuracy of Mahalanobis distance metric has a better performance compared to correlation from k = 1 to k = 5 with the value of 100.00%. This finding is important as it proved the capabilities of k-NN modelling in classifying the agarwood oil quality. Not limited to that, it also contributed to the agarwood oil research area as well as its industry.
format Article
author Noor Syafina, Mahamad Jainalabidin
Aqib Fawwaz, Mohd Amidon
Nurlaila F., Ismail
Zakiah, Mohd Yusoff
Saiful Nizam, Tajuddin
Mohd Nasir, Taib
author_facet Noor Syafina, Mahamad Jainalabidin
Aqib Fawwaz, Mohd Amidon
Nurlaila F., Ismail
Zakiah, Mohd Yusoff
Saiful Nizam, Tajuddin
Mohd Nasir, Taib
author_sort Noor Syafina, Mahamad Jainalabidin
title The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification
title_short The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification
title_full The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification
title_fullStr The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification
title_full_unstemmed The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification
title_sort k-nearest neighbor modelling by varying mahalanobis and correlation in distance metric for agarwood oil quality classification
publisher Intelektual Pustaka Media Utama
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/40219/1/The%20k-nearest%20neighbor%20modelling%20by%20varying%20Mahalanobis.pdf
http://umpir.ump.edu.my/id/eprint/40219/
https://doi.org/10.11591/ijaas.v11.i3.pp242-252
https://doi.org/10.11591/ijaas.v11.i3.pp242-252
_version_ 1822924128520241152
score 13.239859

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