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INDONESIA
Indonesian Journal of Computational Language Studies
Published by CV Narasi Khatulistiwa Indonesia
ISSN : -     EISSN : 31637884     DOI : -
Core Subject :
Indonesian Journal of Computational Language Studies is a double blind peer-reviewed scholarly journal that publishes original research articles and critical studies at the intersection of language, computation, and data-driven methodologies. This journal is published quarterly as a platform for the dissemination of theoretical, empirical, and interdisciplinary findings that explore how computational approaches contribute to the analysis, modeling, and understanding of linguistic phenomena. It addresses a broad range of topics, including but not limited to computational linguistics, natural language processing, corpus linguistics, language modeling, machine learning for language analysis, discourse and text mining, digital humanities, language technologies, and computational approaches to language use across social, cultural, and digital contexts.
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Articles 12 Documents
 1   2 
Small models, many languages: parameter-efficient adaptation of multilingual language models for low-resource Indonesian languages Ainul Hadziqi
Indonesian Journal of Computational Language Studies Vol. 1 No. 2 (2026): Large language models, linguistic diversity, and responsible NLP in Indonesia
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/ijcl.v1i2.916

Abstract

Background: Multilingual language models have expanded computational access to many languages, yet Indonesian local languages remain unevenly represented in corpora, benchmarks, and adaptation pipelines despite Indonesia’s exceptional linguistic diversity. Objective: This study examines how parameter-efficient adaptation can support small multilingual language models for low-resource Indonesian languages by treating corpus adequacy, source provenance, language coverage, and evaluation readiness as central methodological conditions. Method: Using a corpus-level research design, this study maps 20 public source-level items consisting of task datasets, benchmark resources, documentation, registry infrastructure, supplementary corpus portals, and model-context collections for Indonesian and selected local languages. Results: The findings show that task-ready datasets form the strongest part of the corpus frame, while benchmarking, reproducibility, and auxiliary corpus layers remain less evenly distributed. Language coverage is stratified, with Javanese and Sundanese occupying stronger cross-task positions, while Acehnese, Ngaju, Bima, Toba Batak, and Ambonese Malay are better treated as focused transfer-stress cases. Implication: These patterns indicate that parameter-efficient fine-tuning should not be interpreted through model efficiency alone, because adaptation gains remain bounded by corpus distribution and language-specific evidence. Novelty: The novelty of this study lies in repositioning PEFT for Indonesian local languages as a corpus-dependent multilingual adaptation problem rather than a purely architectural optimization problem
Who gets represented by Indonesian AI? measuring regional, gender, and sociolinguistic bias in large language models Mohammad Anis Sumadi
Indonesian Journal of Computational Language Studies Vol. 1 No. 2 (2026): Large language models, linguistic diversity, and responsible NLP in Indonesia
Publisher : CV Narasi Khatulistiwa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67490/ijcl.v1i2.917

Abstract

Background: Indonesia’s regional, gendered, and sociolinguistic diversity raises a critical question about whether large language models represent Indonesian identities with equal specificity, agency, and legitimacy in computational discourse. Objective: This study aims to examine how Indonesian-facing large language models generate representations of regions, gender markers, occupations, and language varieties under controlled prompt conditions. Method: Using a prompt-based audit design, this study analyses 42 prompt units divided into regional, gender-counterfactual, and sociolinguistic conditions, with coding focused on visibility, specificity, agency, competence, register alignment, semantic stability, and language shifting. Results: The findings indicate that regional representation is uneven: some regions are profiled through professional competence, while others are rendered through generic neutrality, cultural tokenisation, peripheral framing, or national homogenisation. Gendered outputs show partial professional parity, but male-coded subjects receive stronger leadership and technical authority, whereas female-coded subjects are more often associated with care, affect, and relational labour. Implication: Sociolinguistic robustness is strongest in formal Indonesian, more adaptive in colloquial Indonesian, and less stable in local-language conditions, where semantic drift, code-mixing, and defaulting to Indonesian appear. Novelty: This study contributes an intersectional audit framework that reframes Indonesian AI bias as a problem of regional visibility, gendered agency, and sociolinguistic legitimacy across culturally stratified AI systems in multilingual Indonesia today.

Page 2 of 2 | Total Record : 12

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