Quadratic tuned kernel parameter in Non-linear support vector machine (SVM) for agarwood oil compounds quality classification

This paper presents the analysis of agarwood oil compounds quality classification by tuning quadratic kernel parameter in Support Vector Machine (SVM). The experimental work involved of agarwood oil samples from low and high qualities. The input is abundances (%) of the agarwood oil compounds and th...

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Main Authors: Bin Mohd Raif, Muhamad Addin Akmal (Author), Ismail, Nurlaila (Author), Mohd Ali, Nor Azah (Author), Fazalul Rahiman, Mohd Hezri (Author), Nizam Tajuddin, Saiful (Author), Nasir Taib, Mohd (Author)
Format: EJournal Article
Published: Institute of Advanced Engineering and Science, 2020-03-01.
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001 ijeecs20929_13519
042 |a dc 
100 1 0 |a Bin Mohd Raif, Muhamad Addin Akmal  |e author 
100 1 0 |e contributor 
700 1 0 |a Ismail, Nurlaila  |e author 
700 1 0 |a Mohd Ali, Nor Azah  |e author 
700 1 0 |a Fazalul Rahiman, Mohd Hezri  |e author 
700 1 0 |a Nizam Tajuddin, Saiful  |e author 
700 1 0 |a Nasir Taib, Mohd  |e author 
245 0 0 |a Quadratic tuned kernel parameter in Non-linear support vector machine (SVM) for agarwood oil compounds quality classification 
260 |b Institute of Advanced Engineering and Science,   |c 2020-03-01. 
500 |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20929 
520 |a This paper presents the analysis of agarwood oil compounds quality classification by tuning quadratic kernel parameter in Support Vector Machine (SVM). The experimental work involved of agarwood oil samples from low and high qualities. The input is abundances (%) of the agarwood oil compounds and the output is the quality of the oil either high or low. The input and output data were processed by following tasks; i) data processing which covers normalization, randomization and data splitting into two parts in which training and testing database (ratio of 80%:20%), and ii) data analysis which covers SVM development by tuning quadratic kernel parameter. The training dataset was used to be train the SVM model and the testing dataset was used to test the developed SVM model. All the analytical works are performed via MATLAB software version R2013a. The result showed that, quadratic tuned kernel parameter in SVM model was successful since it passed all the performance criteria's in which accuracy, precision, confusion matrix, sensitivity and specificity. The finding obtained in this paper is vital to the agarwood oil and its research area especially to the agarwood oil compounds classification system. 
540 |a Copyright (c) 2019 Institute of Advanced Engineering and Science 
540 |a http://creativecommons.org/licenses/by-nc/4.0 
546 |a eng 
690
690 |a SVM, Quadratic, Agarwood oil, Classification, Oil quality 
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
655 7 |2 local 
786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 17, No 3: March 2020; 1371-1376 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v17.i3 
787 0 |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20929/13519 
856 4 1 |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20929/13519  |z Get fulltext