Precise classification of five grades aquilaria malaccensis essential oil: Multiclass support vector machine utilizing pattern graphical representation on a two-dimensional graph - Anis Hazirah ‘Izzati H., Al-Hadi; Noor Aida Syakira, Ahmad Sabri; Nurlaila F., Ismail; Zakiah, Mohd Yusoff; Almisreb, Ali Abd; Saiful Nizam, Tajuddin; Mohd Nasir, Taib (Penerbit UTHM, 2024)
A member of the Thymelaeaceaefamily, Aquilaria Malaccensisis a well-known tree species recognized for its aromatic resinous wood. In Indonesia and Malaysia, the tree is known by local names such "gaharu" and "karas”. Its resinous wood is highly valued for its distinct scent and is commonly used in cultural, religious, and economic settings. The absence of a uniform grading system weakens market stability for agarwood essential oil. Creating a standardized grading system is vital to tackle these problems and maintain the stability the industry. This study aims to demonstrate the effectiveness of Multiclass Support Vector Machine (MSVM) strategies in evaluating agarwood essential oil. The MSVM is recognized as a highly successful classification tool. The MSVM was built using a Radial Basis Function (RBF) as the kernel function in MATLAB2021b. There are 660 data samples for each of the 11 chemical elements in the dataset. The agarwood essential oil is classified into a total of five grades. The research presented in this study demonstrates that the actual and predicted data for five grades do not differ in 5x5 confusion matrix, with the pattern graphical representation being dispersed according to its quality classification. The model attained 100% accuracy, sensitivity, specificity, and precision, as evidenced by a 5x5 confusion matrix in which actual and predicted data are aligned perfectly. The results validate that the MSVM model can consistently categorize the agarwood essential oil quality grades, hence establishing a reliable foundation for grading assessment in the industry.