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Therefore, agarwood oil has high demand and is traded" /> <title>(PDF) The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification | IJAAS Journal - Academia.edu</title> <link rel="canonical" href="https://www.academia.edu/97036638/The_k_nearest_neighbor_modelling_by_varying_Mahalanobis_and_correlation_in_distance_metric_for_agarwood_oil_quality_classification" /> <script async src="https://www.googletagmanager.com/gtag/js?id=G-5VKX33P2DS"></script> <script> window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'G-5VKX33P2DS', { cookie_domain: 'academia.edu', send_page_view: false, }); gtag('event', 'page_view', { 'controller': "single_work", 'action': "show", 'controller_action': 'single_work#show', 'logged_in': 'false', 'edge': 'unknown', // Send nil if there is no A/B test bucket, in case some records get logged // with missing data - that way we can distinguish between the two cases. // ab_test_bucket should be of the form <ab_test_name>:<bucket> 'ab_test_bucket': null, }) </script> <script> var $controller_name = 'single_work'; var $action_name = "show"; var $rails_env = 'production'; var $app_rev = '9387f500ddcbb8d05c67bef28a2fe0334f1aafb8'; var $domain = 'academia.edu'; var $app_host = "academia.edu"; var $asset_host = "academia-assets.com"; var $start_time = new Date().getTime(); var $recaptcha_key = "6LdxlRMTAAAAADnu_zyLhLg0YF9uACwz78shpjJB"; var $recaptcha_invisible_key = "6Lf3KHUUAAAAACggoMpmGJdQDtiyrjVlvGJ6BbAj"; var $disableClientRecordHit = false; </script> <script> window.require = { config: function() { return function() {} } } </script> <script> window.Aedu = window.Aedu || {}; window.Aedu.hit_data = null; window.Aedu.serverRenderTime = new Date(1733000014000); window.Aedu.timeDifference = new Date().getTime() - 1733000014000; </script> <script type="application/ld+json">{"@context":"https://schema.org","@type":"ScholarlyArticle","abstract":"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.","author":[{"@context":"https://schema.org","@type":"Person","name":"IJAAS Journal"}],"contributor":[],"dateCreated":"2023-02-16","dateModified":"2023-02-16","datePublished":"2022-01-01","headline":"The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification","inLanguage":"en","keywords":[],"locationCreated":null,"publication":"International Journal of Advances in Applied Sciences (IJAAS)","publisher":{"@context":"https://schema.org","@type":"Organization","name":null},"image":null,"thumbnailUrl":null,"url":"https://www.academia.edu/97036638/The_k_nearest_neighbor_modelling_by_varying_Mahalanobis_and_correlation_in_distance_metric_for_agarwood_oil_quality_classification","sourceOrganization":[{"@context":"https://schema.org","@type":"EducationalOrganization","name":null}]}</script><link rel="stylesheet" media="all" 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{"work":{"id":97036638,"created_at":"2023-02-16T20:38:59.358-08:00","from_world_paper_id":null,"updated_at":"2023-02-16T21:02:48.495-08:00","_data":{"doi":"10.11591/ijaas.v11.i3.pp242-252","abstract":"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.","publication_date":"2022,,","publication_name":"International Journal of Advances in Applied Sciences (IJAAS)"},"document_type":"paper","pre_hit_view_count_baseline":null,"quality":"high","language":"en","title":"The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [201854234]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "full_page_mobile_sutd_modal"; window.loswp.useOptimizedScribd4genScript = false; window.loswp.appleClientId = 'edu.academia.applesignon';</script><script defer="" src="https://accounts.google.com/gsi/client"></script><div class="ds-loswp-container"><div class="ds-work-card--grid-container"><div class="ds-work-card--container js-loswp-work-card"><div class="ds-work-card--cover"><div class="ds-work-cover--wrapper"><div class="ds-work-cover--container"><button class="ds-work-cover--clickable js-swp-download-button" data-signup-modal="{"location":"swp-splash-paper-cover","attachmentId":98768742,"attachmentType":"pdf"}"><img alt="First page of “The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification”" class="ds-work-cover--cover-thumbnail" src="https://0.academia-photos.com/attachment_thumbnails/98768742/mini_magick20230217-1-yvrjk2.png?1676606544" /><img alt="PDF Icon" class="ds-work-cover--file-icon" src="//a.academia-assets.com/assets/single_work_splash/adobe.icon-574afd46eb6b03a77a153a647fb47e30546f9215c0ee6a25df597a779717f9ef.svg" /><div class="ds-work-cover--hover-container"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span><p>Download Free PDF</p></div><div class="ds-work-cover--ribbon-container">Download Free PDF</div><div class="ds-work-cover--ribbon-triangle"></div></button></div></div></div><div class="ds-work-card--work-information"><h1 class="ds-work-card--work-title">The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification</h1><div class="ds-work-card--work-authors ds-work-card--detail"><a class="ds-work-card--author js-wsj-grid-card-author ds2-5-body-md ds2-5-body-link" data-author-id="201854234" href="https://independent.academia.edu/ijaasjournal"><img alt="Profile image of IJAAS Journal" class="ds-work-card--author-avatar" src="https://0.academia-photos.com/201854234/63099179/51400332/s65_ijaas.journal.jpg" />IJAAS Journal</a></div><div class="ds-work-card--detail"><p class="ds-work-card--detail ds2-5-body-sm">2022, International Journal of Advances in Applied Sciences (IJAAS)</p><a class="js-loswp-work-card-doi-link ds2-5-body-sm ds2-5-body-link" href="https://doi.org/10.11591/ijaas.v11.i3.pp242-252" rel="nofollow">https://doi.org/10.11591/ijaas.v11.i3.pp242-252</a><div class="ds-work-card--work-metadata"><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">visibility</span><p class="ds2-5-body-sm" id="work-metadata-view-count">…</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">description</span><p class="ds2-5-body-sm">11 pages</p></div><div class="ds-work-card--work-metadata__stat"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">link</span><p class="ds2-5-body-sm">1 file</p></div></div><script>(async () => { const workId = 97036638; 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} }; // If the DOM is still loading, wait for it to be ready before updating the view count. if (document.readyState === "loading") { document.addEventListener('DOMContentLoaded', () => { updateViewCount(viewCount); }); // Otherwise, just update it immediately. } else { updateViewCount(viewCount); } })();</script></div><p class="ds-work-card--work-abstract ds-work-card--detail ds2-5-body-md">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.</p><div class="ds-work-card--button-container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--work-card","attachmentId":98768742,"attachmentType":"pdf","workUrl":"https://www.academia.edu/97036638/The_k_nearest_neighbor_modelling_by_varying_Mahalanobis_and_correlation_in_distance_metric_for_agarwood_oil_quality_classification"}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--work-card","attachmentId":98768742,"attachmentType":"pdf","workUrl":"https://www.academia.edu/97036638/The_k_nearest_neighbor_modelling_by_varying_Mahalanobis_and_correlation_in_distance_metric_for_agarwood_oil_quality_classification"}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div></div><div data-auto_select="false" data-client_id="331998490334-rsn3chp12mbkiqhl6e7lu2q0mlbu0f1b" data-doc_id="98768742" data-landing_url="https://www.academia.edu/97036638/The_k_nearest_neighbor_modelling_by_varying_Mahalanobis_and_correlation_in_distance_metric_for_agarwood_oil_quality_classification" data-login_uri="https://www.academia.edu/registrations/google_one_tap" data-moment_callback="onGoogleOneTapEvent" id="g_id_onload"></div><div class="ds-top-related-works--grid-container"><div class="ds-related-content--container ds-top-related-works--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="0" data-entity-id="89850401" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89850401/A_preliminary_study_on_the_intelligent_model_of_k_nearest_neighbor_for_agarwood_oil_quality_grading">A preliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="245422617" href="https://independent.academia.edu/AqibFawwazMohdAmidon">Aqib Fawwaz Mohd Amidon</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="184756992" href="https://independent.academia.edu/ijeecsteam">Indonesian Journal of Electrical Engineering and Computer Science</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Indonesian Journal of Electrical Engineering and Computer Science, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Essential oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. 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The review on k-NN is done to prove the technique is compatible for future research studies based on its performance.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A preliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading","attachmentId":93576916,"attachmentType":"pdf","work_url":"https://www.academia.edu/89850401/A_preliminary_study_on_the_intelligent_model_of_k_nearest_neighbor_for_agarwood_oil_quality_grading","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/89850401/A_preliminary_study_on_the_intelligent_model_of_k_nearest_neighbor_for_agarwood_oil_quality_grading"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="88723094" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/88723094/A_Ppreliminary_study_on_the_intelligent_model_of_k_nearest_neighbor_for_agarwood_oil_quality_grading">A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="167341124" href="https://independent.academia.edu/YusoffZakiah">Zakiah Yusoff</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Indonesian Journal of Electrical Engineering and Computer Science</p><p class="ds-related-work--abstract ds2-5-body-sm">Essential oils extracted from trees has various usages like perfumes, incense, aromatherapy and traditional medicine which increase their popularity in global market. 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The review on k-NN is done to prove the technique is compatible for future res...</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Ppreliminary study on the intelligent model of k-nearest neighbor for agarwood oil quality grading","attachmentId":92644930,"attachmentType":"pdf","work_url":"https://www.academia.edu/88723094/A_Ppreliminary_study_on_the_intelligent_model_of_k_nearest_neighbor_for_agarwood_oil_quality_grading","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/88723094/A_Ppreliminary_study_on_the_intelligent_model_of_k_nearest_neighbor_for_agarwood_oil_quality_grading"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="82284908" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/82284908/k_nearest_neighbor_modelling_of_agarwood_oil_samples_available_in_capital_of_Malaysia_market">k-nearest neighbor modelling of agarwood oil samples available in capital of Malaysia market</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="163474776" href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Electrical and Computer Engineering (IJECE), 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">Agarwood oil is consumed during traditional ceremonies and even in medicinal purposes due to its effective therapeutic characteristic. As a part of ongoing research on agarwood oil, this paper presented a k-nearest neighbor (k-NN) modelling of agarwood oil samples available in the capital of Malaysia market. The work involved agarwood oil samples from three sources which are lab, local manufacturer and market. The inputs are the chemical compounds and the output is the oil's resources. The input-output was divided into training and testing dataset with the ratio of 80% to 20%, respectively, before they were fed to the k-NN for model development as well as model validation. During the model development, the k-value was varied from 1 to 5, and their accuracy was observed. The result showed that the k=1 and k=2 shared the similar accuracy for training and testing datasets, which are 98.63% and 100.00%, respectively. This study revealed the capabilities of the k-NN model in classifying the agarwood oil samples to the three sources: lab, local manufacturer and market. It was a significant study and contributed to further work especially those related to agarwood oil research area.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"k-nearest neighbor modelling of agarwood oil samples available in capital of Malaysia market","attachmentId":88042343,"attachmentType":"pdf","work_url":"https://www.academia.edu/82284908/k_nearest_neighbor_modelling_of_agarwood_oil_samples_available_in_capital_of_Malaysia_market","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/82284908/k_nearest_neighbor_modelling_of_agarwood_oil_samples_available_in_capital_of_Malaysia_market"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="61563195" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/61563195/Modeling_of_agarwood_oil_compounds_based_on_linear_regression_and_ANN_for_oil_quality_classification">Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="163474776" href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Electrical and Computer Engineering (IJECE), 2021</p><p class="ds-related-work--abstract ds2-5-body-sm">Agarwood oil is in increasing demand in Malaysia throughout the world for use in incense, traditional medicine, and perfumes. However, there is still no standardized grading method for agarwood oil. It is vital to grade agarwood oil into high and low quality so that both qualities can be properly differentiated. In the present study, data were obtained from the Forest Research Institute Malaysia (FRIM), Selangor Malaysia and Bioaromatic Research Centre of Excellence (BARCE), Universiti Malaysia Pahang (UMP). The work involves the data from a previous researcher. As a part of ongoing research, the stepwise linear regression and multilayer perceptron have been proposed for grading agarwood oil. The output features of the stepwise regression were the input features for modeling agarwood oil in a multilayer perceptron (MLP) network. A three layer MLP with 10 hidden neurons was used with three different training algorithms, namely resilient backpropagation (RBP), levenberg marquardt (LM) and scaled-conjugate gradient (SCG). All analytical work was performed using MATLAB software version R2017a. It was found that one hidden neuron in LM algorithm performed the most accurate result in the classification of agarwood oil with the lowest mean squared error (MSE) as compared to SCG and RBP algorithms. The findings in this research will be a benefit for future works of agarwood oil research areas, especially in terms of oil quality classification.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Modeling of agarwood oil compounds based on linear regression and ANN for oil quality classification","attachmentId":74558686,"attachmentType":"pdf","work_url":"https://www.academia.edu/61563195/Modeling_of_agarwood_oil_compounds_based_on_linear_regression_and_ANN_for_oil_quality_classification","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/61563195/Modeling_of_agarwood_oil_compounds_based_on_linear_regression_and_ANN_for_oil_quality_classification"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="92661067" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/92661067/The_significance_of_artificial_intelligent_technique_in_classifying_various_grades_of_agarwood_oil">The significance of artificial intelligent technique in classifying various grades of agarwood oil</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="250847811" href="https://independent.academia.edu/AmidonAqibFawwazMohd">Aqib Fawwaz Mohd Amidon</a><span>, </span><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="184756992" href="https://independent.academia.edu/ijeecsteam">Indonesian Journal of Electrical Engineering and Computer Science</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Indonesian Journal of Electrical Engineering and Computer Science, 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">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.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"The significance of artificial intelligent technique in classifying various grades of agarwood oil","attachmentId":95610634,"attachmentType":"pdf","work_url":"https://www.academia.edu/92661067/The_significance_of_artificial_intelligent_technique_in_classifying_various_grades_of_agarwood_oil","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/92661067/The_significance_of_artificial_intelligent_technique_in_classifying_various_grades_of_agarwood_oil"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="116963708" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/116963708/Agarwood_oil_quality_identification_using_artificial_neural_network_modelling_for_five_grades">Agarwood oil quality identification using artificial neural network modelling for five grades</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="163474776" href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Electrical and Computer Engineering (IJECE), 2024</p><p class="ds-related-work--abstract ds2-5-body-sm">Agarwood (Aquilaria Malaccensis) oil stands out as one of the most valuable and highly sought-after oils with a hefty price tag due to its widespread use of fragrances, incense, perfumes, ceremonial practices, medicinal applications and as a symbol of luxury. However, nowadays the conventional method that rely on color alone has its limitations as it yields varying results depending on individual panelists' experiences. Hence, the quality identification system of Agarwood oil using its chemical compounds had been proposed in this study to enhance the precision of the Agarwood oil grades thus addressing the shortcomings of traditional methods. This study indicates that the primary chemical compounds of Agarwood oil encompass ɤ-Eudesmol, ar-curcumene, β-dihydroagarofuran, ϒ-cadinene, α-agarofuran, allo-aromadendrene epoxide, valerianol, α-guaiene, 10-epi-ɤeudesmol, β-agarofuran and dihydrocollumellarin. This study employed artificial neural network analysis with the implementation of Levenberg-Marquardt algorithm to identify the Agarwood oil grades. The study's findings revealed that this modeling system of five grades got 100% accuracies with mean square error of 0.14338×10-08. Notably, this lowest mean square error (MSE) value falls within the best hidden neuron 3. These study outcomes play a pivotal role in highlighting the Levenberg Marquardtartificial neural network (LM-ANN) modeling that contribute to the successful of Agarwood oil quality identification using its chemical compounds.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Agarwood oil quality identification using artificial neural network modelling for five grades","attachmentId":112948170,"attachmentType":"pdf","work_url":"https://www.academia.edu/116963708/Agarwood_oil_quality_identification_using_artificial_neural_network_modelling_for_five_grades","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/116963708/Agarwood_oil_quality_identification_using_artificial_neural_network_modelling_for_five_grades"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="89089556" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/89089556/A_novel_application_of_artificial_neural_network_for_classifying_agarwood_essential_oil_quality">A novel application of artificial neural network for classifying agarwood essential oil quality</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="163474776" href="https://independent.academia.edu/JournalIJECE">International Journal of Electrical and Computer Engineering (IJECE)</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Electrical and Computer Engineering (IJECE), 2022</p><p class="ds-related-work--abstract ds2-5-body-sm">This work studies the agarwood oil classification into high and low quality by using two different techniques. Initially, the Forest Research Institute Malaysia (FRIM) and Universiti Malaysia Pahang (UMP) are where the sample preparation and compound extraction of agarwood oil is collected. The data collections were done from the previous researcher consists of 96 samples from seven significant agarwood oil compounds. The artificial neural network (ANN) and the proposed stepwise regression technique were used in this study. The stepwise regression was done the feature selection and successfully reduced agarwood oil compounds from seven to four. Then, the ANN technique was used to classify agarwood oil into high and low using input from seven and four compounds separately. The performance of ANN with different inputs is compared (ANN with seven inputs compared with ANN with four inputs). All the experimental work was performed using the MATLAB R2017b using the "patternet" implemented Levenberg Marquardt algorithm and ten hidden neurons. It was found that the ANN technique using seven compounds obtained the best performance according to high accuracy and lower mean square error (MSE) value. Finally, 1 hidden neuron in ANN with seven inputs selected as the best neuron for grading the agarwood oil compounds.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A novel application of artificial neural network for classifying agarwood essential oil quality","attachmentId":92952933,"attachmentType":"pdf","work_url":"https://www.academia.edu/89089556/A_novel_application_of_artificial_neural_network_for_classifying_agarwood_essential_oil_quality","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/89089556/A_novel_application_of_artificial_neural_network_for_classifying_agarwood_essential_oil_quality"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="104683265" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/104683265/Stepwise_regression_of_agarwood_oil_significant_chemical_compounds_into_four_quality_differentiation">Stepwise regression of agarwood oil significant chemical compounds into four quality differentiation</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="184756992" href="https://independent.academia.edu/ijeecsteam">Indonesian Journal of Electrical Engineering and Computer Science</a></div><p class="ds-related-work--metadata ds2-5-body-xs">The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), 2023</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper gives precise summary on the application of stepwise regression model based upon the pre-process analysis of boxplot for four chemical compounds into four different qualities of agarwood oil. In the global market, agarwood oil is acknowledged as a pricey and valuable nature product owing to its benefits. Unfortunately, there is no standard grading method for agarwood oil grade classification. Intelligent model in grading the quality of agarwood oil is crucial as one of the efforts to classify the agarwood quality. The main model chosen in this study is stepwise regression by concerned specific parameter which is the value of correlation coefficient, R2. To achieve this goal, four out of eleven significant compounds of agarwood oil that consist of 660 data samples from low, medium low, medium high and high quality are representing the input. The independent variables are X1, X2, X3 and X4 which refer to the ɤ-Eudesmol, 10-epi-ɤ-eudesmol, β-agarofuran and dihydrocollumellarin compounds, respectively. MATLAB software version r2015a has been chosen as the simulation platform for this research work. The result showed that the stepwise regression model has a correlation coefficient of 0.756 and p-value less than 0.05 significance level which successfully passed the performance criteria toward regression value.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Stepwise regression of agarwood oil significant chemical compounds into four quality differentiation","attachmentId":104346419,"attachmentType":"pdf","work_url":"https://www.academia.edu/104683265/Stepwise_regression_of_agarwood_oil_significant_chemical_compounds_into_four_quality_differentiation","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/104683265/Stepwise_regression_of_agarwood_oil_significant_chemical_compounds_into_four_quality_differentiation"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="43868467" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/43868467/Grading_Of_Agarwood_Oil_Quality_Based_On_Its_Chemical_Compounds_Using_Self_Organizing_Map_SOM_">Grading Of Agarwood Oil Quality Based On Its Chemical Compounds Using Self Organizing Map (SOM)</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2688209" href="https://independent.academia.edu/SenthilKumar32">WARSE The World Academy of Research in Science and Engineering</a></div><p class="ds-related-work--metadata ds2-5-body-xs"> International Journal of Advanced Trends in Computer Science and Engineering, 2020</p><p class="ds-related-work--abstract ds2-5-body-sm">Agarwood oil is derived from resin saturated heartwood of Agarwood tree using solvent extraction, hydro-distillation and etc. The oil consists ofcomplex mixtures of chromone derivatives, oxygenated sesquiterpenes and sesquiterpene hydrocarbonsand they are significant in determining the quality of Agarwood oil. In this paper, a study focusing on the determination of high and low grades of Agarwood oil quality using SOMwas carried out. Three chemical compounds namely β-agarofuran, α-agarofuran and 10-epi-φ-eudesmol were used as inputs for SOM. In addition, KNN was used to confirm the Agarwood oil grades. This technique wasthen validated using independent samples. The results show that SOM was able to determine the high and low gradesof Agarwood oil with average silhouette indexes between reasonable and excellent, and KNN was able to confirmthese grades with 100% accuracies. In conclusion, the Agarwood oil quality gradingtechnique based on SOM was successfulin determining the Agarwood oil quality into high and low.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Grading Of Agarwood Oil Quality Based On Its Chemical Compounds Using Self Organizing Map (SOM)","attachmentId":64192125,"attachmentType":"pdf","work_url":"https://www.academia.edu/43868467/Grading_Of_Agarwood_Oil_Quality_Based_On_Its_Chemical_Compounds_Using_Self_Organizing_Map_SOM_","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/43868467/Grading_Of_Agarwood_Oil_Quality_Based_On_Its_Chemical_Compounds_Using_Self_Organizing_Map_SOM_"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="88723301" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/88723301/Comparison_of_ANN_Performance_Towards_Agarwood_Oil_Compounds_Pre_processing_Based_on_Principal_Component_Analysis_PCA_and_Stepwise_Regression_Selection_Method">Comparison of ANN Performance Towards Agarwood Oil Compounds Pre-processing Based on Principal Component Analysis (PCA) and Stepwise Regression Selection Method</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="167341124" href="https://independent.academia.edu/YusoffZakiah">Zakiah Yusoff</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Electrical &amp; Electronic Systems Research, 2021</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Comparison of ANN Performance Towards Agarwood Oil Compounds Pre-processing Based on Principal Component Analysis (PCA) and Stepwise Regression Selection Method","attachmentId":92645119,"attachmentType":"pdf","work_url":"https://www.academia.edu/88723301/Comparison_of_ANN_Performance_Towards_Agarwood_Oil_Compounds_Pre_processing_Based_on_Principal_Component_Analysis_PCA_and_Stepwise_Regression_Selection_Method","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/88723301/Comparison_of_ANN_Performance_Towards_Agarwood_Oil_Compounds_Pre_processing_Based_on_Principal_Component_Analysis_PCA_and_Stepwise_Regression_Selection_Method"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":98768742,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":98768742,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_98768742" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. You can download the paper by clicking the button above.</p></div></div></div></div><div class="ds-sidebar--container js-work-sidebar"><div class="ds-related-content--container"><h2 class="ds-related-content--heading">Related papers</h2><div class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="0" data-entity-id="120415220" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/120415220/MSVM_Modelling_on_Agarwood_Oil_Various_Qualities_Classification">MSVM Modelling on Agarwood Oil Various Qualities Classification</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="250847811" href="https://independent.academia.edu/AmidonAqibFawwazMohd">Aqib Fawwaz Mohd Amidon</a></div><p class="ds-related-work--metadata ds2-5-body-xs">Journal of Electrical &amp; Electronic Systems 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class="ds-related-work--container js-related-work-sidebar-card" data-collection-position="1" data-entity-id="43862645" data-sort-order="default"><a class="ds-related-work--title js-related-work-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/43862645/Linear_Regression_of_Gaharu_Oil_Significant_Compounds_for_Oil_Quality_Differentiation">Linear Regression of Gaharu Oil Significant Compounds for Oil Quality Differentiation</a><div class="ds-related-work--metadata"><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2688209" href="https://independent.academia.edu/SenthilKumar32">WARSE The World Academy of Research in Science and Engineering</a><span>, </span><a class="js-related-work-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="167341124" href="https://independent.academia.edu/YusoffZakiah">Zakiah Yusoff</a></div><p class="ds-related-work--metadata ds2-5-body-xs"> International Journal of Advanced Trends 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