Article Per Year (5 Year)
p-Index From 2020 - 2025
0.562
P-Index
This Author published in this journals
All Journal SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JUPE : Jurnal Pendidikan Mandala Riwayat: Educational Journal of History and Humanities
Husnaini, Azizah Nurul
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Riwayat: Educational Journal of History and Humanities

 1 
Analysis of Vocational High School Students' Skills Through Deep Learning Husnaini, Azizah Nurul; Irianto, Tenri Ugi; Munawwar, Muhammad Subchan
Riwayat: Educational Journal of History and Humanities Vol 8, No 4 (2025): Oktober, In proggress
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i4.49499

Abstract

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.
Co-Authors Hardiani, Wahyu Insani, Nur Hanifah irianto, tenri ugi Muhammad Kholil Munawwar, Muhammad Subchan Ningsih, Kholifatul Novita Sholihah, Lintang Al mar'atus Sidik, Rasyid