Support Vector Machine for Classification: A Mathematical and Scientific Approach in Data Analysis - Yulia Restiani; Joko Purwadi (Postgraduate, University of Mataram, 2024)
In this research, SVM will be used to differentiate between plain nail art designs (class 0), 3D nail art designs (class 1), and hand painting nail art designs (class 2). The dataset used consists of images of nail designs that have been collected and analyzed previously. First, the dataset is divided into three different classes based on the type of nail design. The first class (class 0) includes plain nail art designs, then the second class (class 1) is 3D nail art designs, and the third class (class 2) is hand painting nail art designs. This process is carried out to allow SVM to learn the feature differences between the two types of designs. The data used will be divided into training and testing data and divided into three data division schemes, namely 60/40, 70/30, and 80/20. Based on the results of the research discussed, it can be concluded that classification using the Linear SVM model on three data sharing schemes provides the best level of accuracy on the 80/20 scheme, namely 81.25%. Meanwhile, classification using the non-linear SVM model achieved the highest level of accuracy of 95% in the 80/20 scheme with the RBF Kernel. Thus, the SVM model that is suitable for classifying nail art designs is a non-linear SVM model with the 80/20 scheme. The accuracy results obtained from this research also show that SVM provides good performance in classifying nail art designs.