Implementation of Deep Learning Method Using BERT Model in Career Choice Analysis of Gen Z
DOI:
https://doi.org/10.30865/ijics.v9i2.8917Keywords:
Generation Z, career choice, BERT, sentiment analysis, deep learningAbstract
The development of digital technology has significantly influenced how individuals, particularly Generation Z (born between 1997 and 2012), make career decisions. Faced with an abundance of digital information, many individuals in this cohort experience difficulties in selecting career paths that align with their interests, abilities, and labor market demands. This study analyzes the career preferences of Generation Z using a deep learning approach through the Bidirectional Encoder Representations from Transformers (BERT) model, specifically the IndoBERT variant, which is pre-trained on Indonesian-language data. The research data were collected from textual responses to Google Form questionnaires, focusing on four digital career paths: Software Engineer, Content Creator, Digital Marketing, and Entrepreneur. From 601 data samples, sentiment analysis revealed that 57.85% of the responses were positive, while 42.15% were negative. Classification results indicated that Content Creator was the most preferred career, followed by Entrepreneur, Digital Marketer, and Software Engineer. Model evaluation showed a test accuracy of 51.24%, with better performance in categories that had larger data volumes. These findings demonstrate that IndoBERT is effective in capturing opinions and career tendencies from unstructured text and provides a scientific basis for educational institutions, industries, and policymakers to design more relevant career development strategies in the digital era.
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