Noor Syafina, Mahamad Jainalabidin and Aqib Fawwaz, Mohd Amidon and Nurlaila F., Ismail and Zakiah, Mohd Yusoff and Saiful Nizam, Tajuddin and Mohd Nasir, Taib (2022) The k-nearest neighbor modelling by varying Mahalanobis and correlation in distance metric for agarwood oil quality classification. International Journal of Advances in Applied Sciences, 11 (3). pp. 242-252. ISSN 2252-8814. (Published)
|
Pdf
The k-nearest neighbor modelling by varying Mahalanobis.pdf Available under License Creative Commons Attribution Share Alike. Download (789kB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Sesquiterpene; Aquilaria sinensis; Thymelaeaceae |
| 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 |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 09 Feb 2024 07:47 |
| Last Modified: | 09 Feb 2024 07:47 |
| URI: | http://umpir.ump.edu.my/id/eprint/40219 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |