Article Per Year (5 Year)
p-Index From 2021 - 2026
2.024
P-Index
This Author published in this journals
All Journal Jurnal Inovasi Teknologi Pendidikan JAIS (Journal of Applied Intelligent System) Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Stamina JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Jurnal Pengabdian Teknologi Tepat Guna Jurnal Informatika Progres Journal of Artificial Intelligence and Digital Business Indonesian Journal of Islamic Elementary Education Journal of Information System and Computer
Nugroho, Dicky Anggriawan
Unknown Affiliation
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Medicine & Pharmacology Public Health Social Sciences

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
IDENTIFIKASI BIAS SOSIAL EKONOMI DALAM MODEL BAHASA AI INDONESIA MELALUI ETHICAL PROBING Dwi Kinanti, Fadilah Zahra; Aprilia, Ari Maulida; Aulia, Aldila Rachma; Mustafida, Anis Nadhirotul; Nugroho, Dicky Anggriawan
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.508

Abstract

This study evaluates socioeconomic bias in three large language models (LLMs) that support Indonesian Nusantara, IndoGPT, and SEA-LION using an ethical probing approach. A total of 100 short narrative prompts (4–11 words) were compiled to represent issues of poverty, informal employment, access to education, and regional contexts. Each model output was analyzed using five key indicators: emotional valence, stereotypes, narrative themes, framing, and deontic indicators. The results show that all three models tend to produce neutral responses, especially SEA-LION, which has the highest proportion of neutral responses. However, stereotypes still appeared at almost the same level across all models, indicating that a neutral tone does not guarantee bias-free output. IndoGPT showed the highest use of normative language, while Nusantara more often displayed structural framing and empathetic nuances. In contrast, SEA-LION was the most stable in maintaining neutrality without eliminating implicit stereotypical tendencies. These findings confirm that socioeconomic bias in Indonesian-language LLMs still occurs subtly through deterministic narratives, generalizations, and framing that normalizes the vulnerability of low-income groups. This study provides an initial overview of the direction of generative bias in Indonesian LLMs and highlights the need for broader dataset development, stricter annotation methods, and continuous evaluation for the development of fairer models.
Co-Authors Andung Dwi Haryanto Aprilia, Ari Maulida Aulia, Aldila Rachma Dwi Kinanti, Fadilah Zahra Eko Hari Rachmawanto Imam Prayogo Pujiono Isriani Hardini Kholifatun, Isnaeni Mustafida, Anis Nadhirotul Ramadhani, Mutiara Sofia Rofiah, Nurul Hidayati Sabrina, Arum Rahma Putri Sari, Nurul Husnah Mustika Setyorini, Puspa Dwi Sofia, Lailatul Utomo, Surya Ardhiartha Putro Winasari Winasari Yasmin, Nazhifa Naura Ylsan, Dheandra Tsabitha Yuniar, Ramona Aprilia Zubaidi, Muhammad Fikri Bin