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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Sarah Safitri
Bina Nusantara University
Computer Science & IT

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Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique Devi Fitrianah; Sarah Safitri; Nadzla Andrita Intan Ghayatrie
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp447-455

Abstract

This study examines gender bias in artificial intelligence (AI), focusing on the classification of high school students into science, technology, engineering, and mathematics (STEM) and non-STEM fields. Using Indonesian student Computer Science Department, BINUS Graduate Program – Master of Computer Science, Bina Nusantara University, Jakarta, 11480 data, conditional variational autoencoder (CVAE) and multilabel synthetic minority over-sampling technique (MLSMOTE) were employed for data augmentation to mitigate bias before training gated recurrent unit (GRU) and long short-term memory (LSTM) models for prediction. The combination of MLSMOTE and GRU demonstrated superior performance, achieving accuracies of 93% for female students and 94% for males. These results indicate that MLSMOTE and GRU effectively predict fields of study while addressing gender bias. The findings contribute to advancing fairness in AI systems for education and beyond, ensuring equitable opportunities across diverse applications.
Co-Authors Devi Fitrianah Nadzla Andrita Intan Ghayatrie