Intelligent grading of kaffir lime oil quality using non-linear support vector machine

Nor Syahira Jak Jailani, Zuraida Muhammad, Mohd Hezri Fazalul Rahiman, Mohd Nasir Taib


This paper presents kaffir lime oil quality grading using the intelligent system classification method, a non-linear support vector machine (NSVM). This method classifies the quality kaffir lime oil into two groups: high and low quality, based on their significant chemical compounds. The 90 data of kaffir lime oil were used in this project from high to low quality. The abundance (%) of significant chemical compounds will act as the input and high or low quality as an output. The 90 data will be divided into two sets: training and testing data sets with a ratio of 8:2. The radial basis function (RBF) optimization kernel parameters in NSVM. Using the implementation of MATLAB software version R2020a, all data and analysis work was performed automatically. The results showed that the NSVM model met all performance criteria for 100% accuracy, sensitivity, specificity, and precision.


High/low quality; Kaffir lime oil; Non-linear support vector machine; Radial basis function kernel

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International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578