Trend Analysis and Job Classification in the Field of Artificial Intelligence Using the Support Vector Machine (SVM) Method - Helmy, Ahmad; Muhammad Iqbal (Journal of Computer Science and Research (JoCoSiR), 2024)
The rapid advancement of Artificial Intelligence (AI) has significantly transformed the global job landscape, creating new opportunities while redefining existing roles. This study aims to analyze emerging trends and classify job roles in the AI domain using the Support Vector Machine (SVM) method. A dataset was collected from various online job marketplaces and professional platforms to identify key skills, qualifications, and job categories associated with AI-related professions. The data preprocessing involved text normalization, feature extraction using TF-IDF, and classification modeling through SVM. The experimental results demonstrate that the SVM model achieved high accuracy in categorizing AI-related occupations into predefined job clusters, such as Data Scientist, Machine Learning Engineer, AI Researcher, and AI Product Manager. Furthermore, the trend analysis revealed a growing demand for AI professionals with strong interdisciplinary skills combining data analytics, programming, and domain expertise. These findings provide insights for educational institutions, job seekers, and policymakers to align skill development strategies with the evolving needs of the AI workforce.