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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Paradigma Akuntansi
Safitri, Sarah
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Computer Science & IT Economics, Econometrics & Finance Social Sciences

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FAKTOR-FAKTOR YANG MEMPENGARUHI NILAI PERUSAHAAN PADA PERUSAHAAN INFRASTRUKTUR Safitri, Sarah; Santioso, Linda
Jurnal Paradigma Akuntansi Vol. 7 No. 3 (2025): Juli 2025
Publisher : Fakultas Ekonomi, Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jpa.v7i3.34292

Abstract

The research was conducted to empirically prove the effect of leverage (DER), institutional ownership (IO), profitability (ROA), and company size (SIZE) on firm value (PBV) in infrastructure sector companies listed on the Indonesia Stock Exchange. In the research to obtain a valid sample of 90 data from 38 companies after outliers were carried out using the purposive sampling method. This study tested the hypothesis using multiple linear regression methods and data were processed using SPSS version 16. The results of this study stated that leverage and firm size did not have a significant effect on firm value. Institutional ownership has a significant negative effect on firm value. Profitability has a significant influence and has a positive direction on firm value.
Mitigating gender bias in STEM study field classification using GRU and LSTM with augmented dataset technique Fitrianah, Devi; Safitri, Sarah; Intan Ghayatrie, Nadzla Andrita
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 Intan Ghayatrie, Nadzla Andrita Linda Santioso