Graduate Data Job Classification Using Support Vector Machine with Radial Basis Function Kernel

The escalating rates of unemployment among recent graduates constitute a pressing concern, with farreaching implications for a nation’s future. Graduates often encounter challenges in aligning their skills and interests with suitable positions, while employers grapple with identifying the ideal candidates for their job openings. To address this issue, this study focuses on graduate-job classification using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel, based on graduates’ data. The SVM - RBF model’s performance was evaluated with a consistent C value of 10, while the Gamma value underwent variations (0.125, 0.25, and 0.75). In addition, a linear SVM was included for comparative analysis. Various metrics including classification accuracy, Root Mean Square Error (RMSE), and the receiver operating characteristic (ROC) curve were employed to ascertain the optimal classifier performance. The results indicate that the SVM - RBF model with a Gamma value of 0.125 demonstrated the most robust performance, surpassing SVM - RBF models with Gamma values of 0.25 and 0.75, as well as the linear SVM.