학술논문

Agarwood Oil Quality Grading using OVO Multiclass Support Vector Machine
Document Type
Conference
Source
2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS) Artificial Intelligence and Data Sciences (AiDAS), 2021 2nd International Conference on. :1-5 Sep, 2021
Subject
Computing and Processing
General Topics for Engineers
Support vector machines
Training
Sensitivity
Error analysis
Oils
Mathematical models
Software
Agarwood Oil
Quality Grading
Multiclass
One Versus One
Performance criteria
Language
Abstract
When heard about agarwood oil, it is very familiar with world community because of its beneficial. Unfortunately, there is no any standard grading model of agarwood oil was implemented. As a solution forms, it is very important to come out with a standard of quality classification model for agarwood oil grading's. By continuing of the research for the development of this standard, specific algorithm function has been used to make sure the ability of this model is totally not in doubt. Support vector machine (SVM) has been chosen as a main model and for the specific algorithm function that has been chosen was multiclass function. Then, in the function, the one versus one (OVO) strategy has been used to make multiclass work and can be applied on SVM. The analysis work has involving the data taken from the previous researcher that consists of four classes of agarwood oil quality's samples which are low, medium low, medium high and high quality. So, the output was the classification of quality between low, medium low, medium high or high quality while the input was the abundances (%) of compounds. The desk research has been conducted by using MATLAB software version r2020a for the simulation platform. The result showed that the model by using multiclass function has pass the performance criteria standard. The verdict in this research for sure will be valuable for the future research works of agarwood oil areas, especially quality classification part.