Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique

Devi Fitrianah, Sarah Safitri, Nadzla Andrita Intan Ghayatrie

Open source

DOI
10.11591/ijict.v15i2.pp447-455
Published
2026-06-01
Container
International Journal of Informatics and Communication Technology (IJ-ICT)
Publisher
Institute of Advanced Engineering and Science
Open access
unknown

Credibility signals

uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.11591/ijict.v15i2.pp447-455,
  title = {Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique},
  author = {Devi Fitrianah and Sarah Safitri and Nadzla Andrita Intan Ghayatrie},
  year = {2026},
  journal = {International Journal of Informatics and Communication Technology (IJ-ICT)},
  doi = {10.11591/ijict.v15i2.pp447-455},
  url = {https://doi.org/10.11591/ijict.v15i2.pp447-455}
}

RIS

TY  - JOUR
TI  - Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique
AU  - Devi Fitrianah
AU  - Sarah Safitri
AU  - Nadzla Andrita Intan Ghayatrie
PY  - 2026
JO  - International Journal of Informatics and Communication Technology (IJ-ICT)
DO  - 10.11591/ijict.v15i2.pp447-455
UR  - https://doi.org/10.11591/ijict.v15i2.pp447-455
ER  - 

APA

Fitrianah, D., Safitri, S., & Ghayatrie, N. A. I. (2026). Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique. International Journal of Informatics and Communication Technology (IJ-ICT). https://doi.org/10.11591/ijict.v15i2.pp447-455

Source records