Comparison of Different Kernel Parameters using Support Vector Machine for Agarwood Oil Grading

• Published in: 2021 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2021 - Proceedings
• Main Author: 2-s2.0-85112521426
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112521426&doi=10.1109%2fI2CACIS52118.2021.9495869&partnerID=40&md5=b4f6b02423ec7fcddeb531552b92badf

These days, agarwood oil becoming a high demand throughout the world and Malaysia is not excluded. It happens due to the variety of usages such as incense, traditional medicine, and perfumes. However, there has been a lack of research on the development of agarwood oil because there is no any standard grading method of agarwood oil was implemented. As a solution forms, it is very important to come out with a standard method of quality classification for agarwood oil grading's. By continuing of the research for the development of this standard, the comparison of different type of kernel parameter on nonlinear data based on performance measure has been the main objective of this paper. Support Vector Machine (SVM) has been selected as intelligent technique to comparing the output of different type of kernel parameter used. The analysis work has involving the data taken from the previous researcher that consists of two classes of agarwood oil quality's samples which is high and low quality. For the output of this research was the classification of two different quality while the input was the different percentage of the compounds added. The desk research has been conducted by using a software application named MATLAB with version R2016a. The research indicates that each of different kernel parameter used have pass the performance measures standard. The verdict in this research for sure will be valuable for the future research works of agarwood oil areas, especially quality classification part. © 2021 IEEE.