Decision Tree on Branch Node Observations for Significant Compounds of Agarwood Oil Different Grades
For ages, agarwood oil has been utilized in various applications, including scented goods such as incense and fragrances, as well as medicinal therapies. There is currently no accepted method for precisely grading agarwood oil based on its chemical constituents, despite the fact that demand for it is constantly rising. Consequently, the goal of this work is to create a revolutionary method for classifying agarwood oil according to its chemical components. This study created a machine learning technique based on the Decision Tree (DT) classification algorithm to address this issue. The DT model was based on branch node observations, with corresponding values of 11 for MinParent. The result showed that the accuracy of the DT model for the branch node is 94.74%. The performance of the model was evaluated using confusion matrix, accuracy, resubstitution error and cross-validation error in MATLAB software version R2021a. This study revealed the DT as an effective method for classifying agarwood oil grades based on chemical compounds, which has the potential to benefit the agarwood oil industry and research field greatly.