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Intelligent grading of kaffir lime oil quality using non-linear support vector machine

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 6 (2022)

DOI
10.11591/ijece.v12i6.pp6716-6723
ISSN
2722-2578, 2088-8708
Published
2022-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This paper presents kaffir lime oil quality grading using the intelligent system classification method, a non-linear support vector machine (NSVM). This method classifies the quality kaffir lime oil into two groups: high and low quality, based on their significant chemical compounds. The 90 data of kaffir lime oil were used in this project from high to low quality. The abundance (%) of significant chemical compounds will act as the input and high or low quality as an output. The 90 data will be divided into two sets: training and testing data sets with a ratio of 8:2. The radial basis function (RBF) optimization kernel parameters in NSVM. Using the implementation of MATLAB software version R2020a, all data and analysis work was performed automatically. The results showed that the NSVM model met all performance criteria for 100% accuracy, sensitivity, specificity, and precision.

Collaboration network of the authors

32 authors · 115 co-authorship links · 6 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universiti Teknologi MARA
  • International University of Sarajevo
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Nurlaila IsmailUniversiti Teknologi MARA5
Zakiah Mohd YusoffUniversiti Teknologi MARA4
Mohd Azri Abdul AzizUniversiti Teknologi MARA3
Muhamad Aqif Hadi AliasUniversiti Teknologi MARA3
Najidah HambaliUniversiti Teknologi MARA3
Hasnain AbdullahUniversiti Teknologi MARA3
Azizah Hanom AhmadUniversiti Teknologi MARA3
Linda Mohd KasimUniversiti Teknologi MARA3
Megat Syahirul Amin Megat AliUniversiti Teknologi MARA2
Aisyah Hartini JahidinUniversiti Teknologi MARA2
Nooritawati Md TahirUniversiti Teknologi MARA2
Noratikah Zawani MahabobUniversiti Teknologi MARA2
Aqib Fawwaz Mohd AmidonUniversiti Teknologi MARA2
Saiful Nizam TajuddinAffiliation unknown2
Ali Abd AlmisrebInternational University of Sarajevo2
Norhayati Mohamad NoorUniversiti Teknologi MARA2
Siti Mariatul Hazwa Mohd HuzirUniversiti Teknologi MARA1
Rozita JailaniUniversiti Teknologi MARA1
Noor Aida Syakira Ahmad SabriUniversiti Teknologi MARA1
Nik Fasha Edora Nik KamaruzamanUniversiti Teknologi MARA1
Ahmad Ihsan Mohd YassinUniversiti Teknologi MARA1
Nur Shafikah RosliUniversiti Teknologi MARA1
Samihah AbdullahUniversiti Teknologi MARA1
Shafaq Mardhiyana Mohamat KasimUniversiti Teknologi MARA1
Norhayati Mohd NoorAffiliation unknown1
Nazirah Mohamat KasimUniversiti Teknologi MARA1
Noor Azila IsmailUniversiti Teknologi MARA1
Nur Qaisarah AnuarUniversiti Teknologi MARA1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref 
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Intelligent grading of kaffir lime oil quality using non-linear support vector machine</dc:title><dc:creator>Jak Jailani, Nor Syahira</dc:creator><dc:creator>Muhammad, Zuraida</dc:creator><dc:creator>Fazalul Rahiman, Mohd Hezri</dc:creator><dc:creator>Taib, Mohd Nasir</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span lang="EN-US"&amp;gt;This paper presents kaffir lime oil quality grading using the intelligent system classification method, a non-linear support vector machine (NSVM). This method classifies the quality kaffir lime oil into two groups: high and low quality, based on their significant chemical compounds. The 90 data of kaffir lime oil were used in this project from high to low quality. The abundance (%) of significant chemical compounds will act as the input and high or low quality as an output. The 90 data will be divided into two sets: training and testing data sets with a ratio of 8:2. The radial basis function (RBF) optimization kernel parameters in NSVM. Using the implementation of MATLAB software version R2020a, all data and analysis work was performed automatically. The results showed that the NSVM model met all performance criteria for 100% accuracy, sensitivity, specificity, and precision.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2022-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v12i6.pp6716-6723</dc:identifier><dc:identifier>10.11591/ijece.v12i6.pp6716-6723</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 12 No 6 (2022); 6716</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/28749/16159</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>