Anis Hazirah Izzati Hasnu, Al-Hadi and Aqib Fawwaz, Mohd Amidon and Siti Mariatul Hazwa, Mohd Huzir and Nurlaila, Ismail and Zakiah, Mohd Yusoff and Saiful Nizam, Tajuddin and Mohd Nasir, Taib (2023) Preliminary study on agarwood essential oil and its classification techniques using machine learning. Indonesian Journal of Electrical Engineering and Computer Science, 29 (2). pp. 753-760. ISSN 2502-4752. (Published)
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Abstract
Using essential oils derived from trees for pharmaceutical purposes, incense, aromatherapy, and other areas has expanded its popularity on the international market. However, since human sensory evaluation is still the primary technique used to grade essential oils in Malaysia, the classification technique for determining their grade is still below standard. Nonetheless, prior studies established new approaches for classifying the grade of essential oils by studying their chemical compounds. Therefore, agarwood essential oil was selected for the suggested model due to the increasing demand and the high cost of the world's natural raw materials. The support vector machine (SVM) using one versus all (OVA) approach was selected as the classifier for agarwood essential oil. This study provides an overview of essential oils and their prior research techniques. In addition, a review of SVM is conducted to demonstrate that the technique is appropriate for future studies.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Agarwood essential oil; Chemical compounds; Grading; Multiclass classifier; Support vector machine |
| Subjects: | H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management Q Science > Q Science (General) T Technology > T Technology (General) |
| Faculty/Division: | Faculty of Industrial Sciences And Technology Institute of Postgraduate Studies |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 04 Sep 2023 06:19 |
| Last Modified: | 04 Sep 2023 06:19 |
| URI: | http://umpir.ump.edu.my/id/eprint/38109 |
| Download Statistic: | View Download Statistics |
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