Preliminary study on agarwood essential oil and its classification techniques using machine learning

• Main Authors: Anis Hazirah Izzati Hasnu, Al-Hadi, Aqib Fawwaz, Mohd Amidon, Siti Mariatul Hazwa, Mohd Huzir, Nurlaila, Ismail, Zakiah, Mohd Yusoff, Saiful Nizam, Tajuddin, Mohd Nasir, Taib
• Format: Article
• Language: English
• Published: Institute of Advanced Engineering and Science 2023
• Subjects: HD28 Management. Industrial Management; Q Science (General); T Technology (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/38109/1/Preliminary%20study%20on%20agarwood%20essential%20oil.pdf; http://umpir.ump.edu.my/id/eprint/38109/; https://doi.org/10.11591/ijeecs.v29.i2.pp753-760; https://doi.org/10.11591/ijeecs.v29.i2.pp753-760
• Institution: Universiti Malaysia Pahang

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.