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<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" lang="en-US" xml:lang="en-US"> <head> <title>The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification | Jainalabidin | International Journal of Advances in Applied Sciences</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta name="description" content="The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification" /> <link rel="icon" href="https://ijaas.iaescore.com/public/journals/13/journalFavicon_en_US.png" type="image/png" /> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Contributor.Sponsor" xml:lang="en" content=""/> <meta name="DC.Creator.PersonalName" content="Noor Syafina Mahamad Jainalabidin"/> <meta name="DC.Creator.PersonalName" content="Aqib Fawwaz Mohd Amidon"/> <meta name="DC.Creator.PersonalName" content="Nurlaila Ismail"/> <meta name="DC.Creator.PersonalName" content="Zakiah Mohd Yusoff"/> <meta name="DC.Creator.PersonalName" content="Saiful Nizam Tajuddin"/> <meta name="DC.Creator.PersonalName" content="Mohd Nasir Taib"/> <meta name="DC.Date.created" scheme="ISO8601" content="2022-09-01"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2022-03-22"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2022-09-01"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2022-08-29"/> <meta name="DC.Description" xml:lang="en" content="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."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="20612"/> <meta name="DC.Identifier.pageNumber" content="242-252"/> <meta name="DC.Identifier.DOI" content="10.11591/ijaas.v11.i3.pp242-252"/> <meta name="DC.Identifier.URI" content="https://ijaas.iaescore.com/index.php/IJAAS/article/view/20612"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2022 Institute of Advanced Engineering and Science (IAES)" /> <meta name="DC.Rights" content="http://creativecommons.org/licenses/by-sa/4.0"/> <meta name="DC.Source" content="International Journal of Advances in Applied Sciences"/> <meta name="DC.Source.ISSN" content="2722-2594"/> <meta name="DC.Source.Issue" content="3"/> <meta name="DC.Source.URI" content="https://ijaas.iaescore.com/index.php/IJAAS"/> <meta name="DC.Source.Volume" 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href="https://ijaas.iaescore.com/index.php/IJAAS/issue/view/567" target="_parent">Vol 11, No 3</a> > <a href="https://ijaas.iaescore.com/index.php/IJAAS/article/view/20612/0" class="current" target="_parent">Jainalabidin</a> </div> <div id="content"> <div id="topBar"> </div> <div id="articleTitle"><h3>The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification</h3></div> <div id="authorString"><em>Noor Syafina Mahamad Jainalabidin, Aqib Fawwaz Mohd Amidon, Nurlaila Ismail, Zakiah Mohd Yusoff, Saiful Nizam Tajuddin, Mohd Nasir Taib</em></div> <br /> <div id="articleAbstract"> <h4>Abstract</h4> <br /> <div>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.</div> <br /> </div> <div id="articleFullText"> <h4>Full Text:</h4> <a href="https://ijaas.iaescore.com/index.php/IJAAS/article/view/20612/12984" class="file" target="_parent">PDF</a> </div> <br /> <br /> DOI: <a id="pub-id::doi" href="http://doi.org/10.11591/ijaas.v11.i3.pp242-252">http://doi.org/10.11591/ijaas.v11.i3.pp242-252</a> <div class="separator"></div> <h3>Refbacks</h3> <ul> <li>There are currently no refbacks.</li> </ul> <br /><br /> <p><strong>International Journal of Advances in Applied Sciences (IJAAS)</strong><br />p-ISSN 2252-8814, e-ISSN 2722-2594<br />This journal is published by the聽<a title="IAES" href="https://iaes.or.id/" target="_blank">Institute of Advanced Engineering and Science (IAES)</a>聽in collaboration with聽<a title="IPMU" href="https://ipmuonline.com/" target="_blank">Intelektual Pustaka Media Utama (IPMU)</a>.</p><p><a href="http://creativecommons.org/licenses/by-sa/4.0/" rel="license"><img style="border-width: 0;" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" alt="Creative Commons License" /></a><br />This work is licensed under a <a href="http://creativecommons.org/licenses/by-sa/4.0/" rel="license">Creative Commons Attribution-ShareAlike 4.0 International License</a>.</p><p><br /> <a title="Web Analytics" href="https://statcounter.com/" target="_blank"><img src="https://c.statcounter.com/11952265/0/50f5385a/0/" alt="Web Analytics" /></a> <a href="https://statcounter.com/p11952265/?guest=1">View IJAAS Stats</a></p><script type="text/javascript" src="https://www.googletagmanager.com/gtag/js?id=UA-202831616-1">// <![CDATA[ // ]]></script><script type="text/javascript">// <![CDATA[ window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-202831616-1'); // ]]></script> </div> </div> </div> </div>  </body> </html>