Analysis of Vocational High School Students' Skills Through Deep Learning - Husnaini, Azizah Nurul; Irianto, Tenri Ugi; Munawwar, Muhammad Subchan (Universitas Syiah Kuala, 2025)
This study explores the application of deep learning techniques in assessing and enhancing the skills of vocational high school (VHS) students. Vocational education plays a critical role in preparing students for the workforce, and the integration of artificial intelligence, particularly deep learning, has the potential to transform how students practical and theoretical skills are evaluated. Through a comprehensive review of existing literature, this research investigates the effectiveness of deep learning models, such as deep neural networks (DNNs) and convolutional neural networks (CNNs), in predicting and assessing vocational students' competencies. The findings reveal that deep learning offers promising accuracy in skill prediction and personalized feedback, with applications ranging from automated grading systems to skill-specific assessments in technical fields. However, challenges such as data quality, model interpretability, and integration with traditional education systems remain significant obstacles. The study concludes with recommendations for further research, including expanding deep learning applications to real-time assessments and hybrid evaluation methods. Overall, this research highlights the potential of deep learning to enhance vocational education but underscores the need for addressing existing challenges to ensure its effective implementation.