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p-Index From 2021 - 2026
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This Author published in this journals
All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Nadzla Andrita Intan Ghayatrie
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.
An integration clustering and multi-target classification approach to explore employability and career linearity Nadzla Andrita Intan Ghayatrie; Devi Fitrianah
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i1.pp189-197

Abstract

This study analyzes job placement waiting times and job linearity among female science, technology, engineering, and mathematics (STEM) graduates using clustering and multi-target classification (MTC) models. The K-means least trimmed square (LTS) algorithm, known for its robustness against outliers, was employed for clustering. With k = 2 and a trimming percentage of 30%, the model achieved a silhouette score of 77%, resulting in two distinct clusters: ideal and non-ideal. To enhance the dataset for classification, synthetic data was generated using the adaptive synthetic (ADASYN)-gaussian method. Principal component analysis (PCA) was used for visualization purposes, along with overlapping histograms, to illustrate that the synthetic data distribution closely resembled the original. For classification, a random forest (RF) model was used to predict both jobs waiting time and job linearity. Hyperparameter tuning produced an optimal model with a classification accuracy of 92%. Cross-validation (CV) confirmed the model’s robustness, with F1-micro and F1-macro scores of 94% and 93%, respectively. Results show that although women in STEM are underrepresented, 73% of the female alumni analyzed belonged to the short job waiting group. Furthermore, a strong negative correlation between GPA and job waiting time suggests that higher-GPA graduates tend to secure employment more quickly.
Co-Authors Devi Fitrianah Sarah Safitri