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Achmad Fawaid
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achmad_fawaid.linguistik@upnjatim.ac.id
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+6282318007953
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senalaupn@gmail.com
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Jl. Rungkut Madya, Gn. Anyar, Kec. Gn. Anyar, Surabaya, Jawa Timur 60294
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INDONESIA
Prosiding SENALA (Seminar Nasional Linguistik Indonesia)
Published by Universitas Pembangunan Nasional Veteran Jawa Timur
ISSN : -     EISSN : 3123335X     DOI : -
Core Subject :
Prosiding SENALA focuses on publishing scholarly works presented at the Seminar Nasional Linguistik Indonesia, particularly in the fields of linguistics and language studies. This proceeding aims to serve as an academic forum for researchers, lecturers, students, and language practitioners to share research findings, conceptual frameworks, and innovations in the discipline of linguistics. The scope of Prosiding SENALA includes, but is not limited to: 1. Theoretical Linguistics (Phonology, Morphology, Syntax, Semantics, Pragmatics) 2. Applied Linguistics (Language teaching, Curriculum design, Instructional material development, Language learning assessment 3. Sociolinguistics and Anthropological Linguistics (Language and society, Language variation, Language contact, Local and regional languages, Language revitalization) 4. Psycholinguistics and Neurolinguistics (Language acquisition, Bilingualism, Language and cognition) 5. Discourse Analysis and Critical Linguistics (Textual studies, Media discourse, Political discourse, Cultural discourse) 6. Translation and Interpreting Studies (Translation theory and practice, Interpreting theory and practice) 7. Language Technology and Digital Linguistics (Corpus linguistics, Natural Language Processing, Artificial intelligence for language, E-learning in linguistics) 8. Multidisciplinary Studies (Interdisciplinary approaches between linguistics, education, literature, law, religion, and other social-humanities disciplines)
Arjuna Subject : -
Articles 21 Documents
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Harnessing Big Data for Corpus Linguistics: Redefining Language Patterns and Usage in the Digital Age Yahya Aulia Abdillah
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 1 (2024): Linguistik Indonesia dalam Lanskap Teknologi Digital
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

Corpus linguistics has long relied on the systematic collection and analysis of large text datasets to uncover patterns of language use. In the era of Big Data, this discipline undergoes a significant transformation, as the availability of massive digital corpora fundamentally changes the scope, methods, and applications of linguistic research. This study explores how Big Data reshapes corpus linguistics in terms of scale, representativeness, and analytical possibilities. Using examples from large-scale corpora derived from social media, online news, and digital archives, the paper demonstrates how linguistic patterns can now be analyzed with greater precision and across diverse contexts. The methodological section introduces computational approaches, such as natural language processing (NLP) tools and machine learning algorithms, that enhance corpus analysis. The results highlight novel findings in lexical variation, discourse structures, and language change over time, made possible by Big Data analytics. The discussion critically evaluates the advantages and challenges of this transformation, including issues of data quality, ethics, and accessibility. The conclusion suggests that corpus linguistics, when integrated with Big Data methodologies, not only advances linguistic theory but also has practical implications for education, policy, and digital communication.
Advances in Computational Linguistics through Big Data: Deep Learning Approaches to Natural Language Processing M. Noer Fadli Hidayat; Abu Thalib
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 1 (2024): Linguistik Indonesia dalam Lanskap Teknologi Digital
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

Computational linguistics has entered a transformative era with the integration of Big Data and deep learning. Traditional approaches to natural language processing (NLP) relied on rule-based systems and limited corpora, often constrained by linguistic coverage and scalability. The advent of Big Data has made it possible to train large-scale neural architectures capable of modeling complex linguistic phenomena across diverse languages and domains. This article examines how Big Data-driven deep learning advances computational linguistics in three key areas: semantic representation, language generation, and cross-linguistic modeling. Using data from large-scale repositories, including multilingual web corpora and open-source datasets, we demonstrate how deep neural networks outperform traditional models in both accuracy and adaptability. The results highlight not only technical progress but also challenges related to interpretability, bias, and ethical implications. We argue that computational linguistics, strengthened by Big Data, is moving beyond descriptive modeling to predictive and generative capabilities that reshape communication technologies, education, and cross-cultural understanding.
Big Data and the Transformation of Media Literacy: Linguistic Insights into Digital Communication Practices Farhan; Mualim Wijaya
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 1 (2024): Linguistik Indonesia dalam Lanskap Teknologi Digital
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

 The digital revolution has reshaped how media is produced, consumed, and interpreted, presenting new challenges and opportunities for media literacy. In the era of Big Data, vast amounts of linguistic and multimodal data are generated through social media platforms, news portals, and online interactions.  This study investigates how Big Data transforms media literacy by analyzing digital communication practices from a linguistic perspective.  Drawing upon large-scale corpora of online discourse, the research employs computational and discourse-analytic methods to explore patterns of language use, misinformation, and participatory communication. Results reveal how Big Data enhances the capacity to detect narrative framing, identify misinformation, and understand audience engagement. At the same time, ethical concerns regarding privacy, data bias, and algorithmic influence raise critical questions about equitable access to digital knowledge. The findings suggest that media literacy, when supported by Big Data analytics, transcends traditional critical reading skills and evolves into a dynamic competence that integrates linguistic analysis, critical thinking, and digital ethics.  This transformation has significant implications for education, public discourse, and democratic participation in an increasingly datafied world.
The Role of Big Data in Legal Linguistics: Enhancing Access to Justice through Automated Textual Analysis Ismail Marzuki
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 1 (2024): Linguistik Indonesia dalam Lanskap Teknologi Digital
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

 The intersection of language and law has traditionally relied on close reading of legal documents, statutes, and case law. In the era of Big Data, however, legal linguistics is undergoing a major transformation as millions of judicial texts, contracts, and online communications become accessible for computational analysis. This study investigates how Big Data transforms media literacy by analyzing digital communication practices from a linguistic perspective.  Drawing on corpora of judicial opinions, legislative records, and online dispute resolution texts, the research employs natural language processing (NLP) and corpus-based methods to analyze legal discourse at scale. Results demonstrate how Big Data enhances the detection of legal ambiguities, improves information retrieval for case law, and supports predictive models for judicial outcomes. Yet, issues of bias, privacy, and algorithmic accountability remain central challenges. The discussion emphasizes that Big Data-driven legal linguistics must balance technological efficiency with principles of fairness and transparency.  Ultimately, the integration of Big Data into legal linguistics holds promise not only for advancing research but also for democratizing access to legal information in society.
Big Data in Forensic Linguistics: Improving Authorship Attribution and Threat Detection in Cyber Contexts Moh Atikurrahman
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 1 (2024): Linguistik Indonesia dalam Lanskap Teknologi Digital
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

Forensic linguistics, the study of language in legal and investigative contexts, has gained increasing relevance in the digital era. The proliferation of online communication has created both challenges and opportunities for authorship attribution and threat detection.  This study explores how Big Data enhances forensic linguistic practices by enabling large-scale analysis of digital texts, such as emails, chat messages, and social media posts.  Using natural language processing (NLP), stylometry, and machine learning techniques, we analyze millions of documents to identify linguistic fingerprints, detect threatening language, and attribute authorship in cybercrime cases.  Results demonstrate that Big Data improves accuracy in identifying authorship through stylistic markers and enhances the detection of threats by analyzing lexical, syntactic, and pragmatic patterns. However, ethical concerns—including privacy, consent, and the risk of algorithmic bias—pose significant challenges. This article argues that Big Data-driven forensic linguistics represents a powerful tool for law enforcement and legal proceedings, but its application must be guided by strict ethical frameworks.  By combining linguistic theory, computational models, and Big Data analytics, forensic linguistics can significantly contribute to cybercrime prevention and the protection of digital communities.
Linguistic Big Data Analytics for Combating Cybercrime: Detecting Fraud, Hate Speech, and Online Deception Achmad Naufal Irsyadi
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 1 (2024): Linguistik Indonesia dalam Lanskap Teknologi Digital
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

The rise of cybercrime has created unprecedented challenges for governments, law enforcement, and digital communities. Traditional investigative approaches, limited by scale and speed, struggle to keep pace with the volume and velocity of online communication. In the era of Big Data, linguistic analysis emerges as a powerful tool for combating cybercrime by identifying fraud, hate speech, and online deception. This article explores how natural language processing (NLP), corpus-based methods, and machine learning techniques are applied to massive digital datasets to detect malicious communication patterns. Drawing on large corpora of phishing emails, extremist forums, and social media platforms, the study demonstrates how linguistic fingerprints—such as lexical markers, syntactic anomalies, and discourse structures—reveal deceptive practices and harmful content. Results highlight significant improvements in detection accuracy compared to traditional methods, but also point to challenges related to multilingual data, adversarial obfuscation, and ethical concerns of surveillance. The discussion argues that while Big Data analytics strengthens the fight against cybercrime, it must be guided by ethical safeguards to balance digital security with privacy rights. Ultimately, Big Data-driven forensic linguistics represents both a technological advancement and a societal responsibility in ensuring safer digital environments.
Clinical Linguistics in the Era of Big Data: AI-Assisted Diagnosis of Speech and Language Disorders Arvina Dwi Romadhani
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 1 (2024): Linguistik Indonesia dalam Lanskap Teknologi Digital
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

 Clinical linguistics has traditionally relied on detailed case studies, manual transcription, and expert interpretation to diagnose speech and language disorders. While effective in clinical settings, these approaches are often time-consuming, subjective, and limited in scalability. The rise of Big Data and natural language processing (NLP) has introduced transformative possibilities for clinical linguistics, enabling automated, large-scale analysis of speech samples and linguistic data. This article examines how Big Data-driven AI systems contribute to the diagnosis of communication disorders, with a focus on aphasia, dysarthria, and developmental language disorders. The study utilized three datasets: (1) the AphasiaBank corpus containing transcripts of individuals with aphasia (MacWhinney et al., 2011), (2) a motor speech disorder dataset including dysarthric speech samples, and (3) a developmental language disorder corpus from pediatric clinical assessments. Results demonstrate that Big Data-enhanced models outperform traditional manual methods in diagnostic accuracy and speed, though challenges of data privacy, interpretability, and clinical acceptance remain. The discussion emphasizes that AI-assisted clinical linguistics should complement, rather than replace, expert judgment. By integrating computational models with clinical expertise, Big Data offers significant promise for improving early diagnosis, personalized treatment, and accessibility to speech-language services.
Transforming Language Education with Big Data: Adaptive Learning Analytics for Student Centered Pedagogy Achmad Fawaid; Ahmad Zubaidi
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 1 (2024): Linguistik Indonesia dalam Lanskap Teknologi Digital
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

The incorporation of Big Data into language education has reshaped how learning processes are organized, delivered, and assessed. Conventional teacher-centered models, which often rely on uniform instruction, are limited in addressing the diverse needs of learners in multilingual and digitally mediated settings. This study examines the role of Big Data-driven adaptive learning analytics in advancing student-centered pedagogy. Using extensive datasets from learning management systems (LMS), online assessments, and learner interaction logs, predictive modeling and discourse analysis were applied to identify learning behaviors, personalize instructional materials, and monitor progress. The findings show that adaptive analytics significantly enhance student participation, retention, and achievement when compared with traditional static approaches. At the same time, issues such as data security, algorithmic bias, and the preparedness of educators present ongoing challenges. The study concludes that, when guided by ethical considerations and integrated into pedagogical practice, adaptive learning analytics powered by Big Data can transform language education into a more dynamic, inclusive, and learner-focused system that supports autonomy and long-term learning development.
Acronymization of Political Terms in The Jakarta Post: A Morphological Study of Political News Discourse Rizki Hardiyanti
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 2 (2025): Transformasi Linguistik di Era Big Data
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

Acronymization has become increasingly prominent in contemporary political journalism, where complex institutional terms are condensed for brevity and immediacy. Although widely perceived as a neutral linguistic device, acronyms can also function as ideological tools that subtly frame political narratives. This study aims to analyze the morphological processes and discursive functions of political acronyms in The Jakarta Post. Method: Using Yule’s framework of word formation, the study examined political news articles published in July 2025 and identified 39 political acronyms, which were classified into Initialisms, Acronyms, and Multiple-Process formations. The corpus was then analyzed through Fairclough’s Critical Discourse Analysis (CDA) to determine how these forms operate within political discourse. The results indicate that Initialisms are the most dominant word formation process in the data. At the discursive level, acronymization in The Jakarta Post is shown to serve not only textual efficiency but also broader ideological functions. Acronyms carry historical and political connotations that shape public interpretation by foregrounding authority, institutional identity, and power relations. The study concludes that acronymization in political reporting is both a linguistic and ideological practice that contributes to constructing political meaning within media discourse. This highlights its significance as a communicative strategy in an era of media convergence and digitalized political communication.
Critical Discourse Analysis of Teun A. van Dijk’s Model in the Lyrics of Preambule by The Brandals Aidil Fitrian; Salmi Miftah Hidayah
Prosiding SENALA (Seminar Nasional Linguistik Indonesia) Vol. 2 (2025): Transformasi Linguistik di Era Big Data
Publisher : Prodi Linguistik Indonesia UPN "Veteran" Jawa Timur

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Abstract

Music often serves as a medium for social and political criticism. The song Preambule by The Brandals was chosen because it contains a discourse of resistance against oligarchy, corruption, and the decline of democracy in Indonesia after the reform era. In the context of popular culture, alternative music becomes a space for the articulation of ideology and social awareness of the inequality of power structures. This study aims to identify the text structure, social cognition, and social context in the lyrics of Preambule to reveal how the discourse of criticism against oligarchy is represented and interpreted as a form of cultural resistance. This study uses a qualitative descriptive method with text analysis techniques through three dimensions: text structure (macro, superstructure, micro), social cognition, and social context. The results of the study show that the lyrics of Preambule represent criticism of oligarchy, collusion, nepotism, and repression of civil society. The song's creator, Eka Annash, expresses his concern about the decline of democracy and social inequality. The social context of this song is closely related to the political conditions in Indonesia, especially after the passing of the Job Creation Bill and the rise of identity politics. The song Preambule is not merely an artistic work, but also a medium of resistance and public awareness of the dangers of oligarchy. This finding confirms that music can function as ideological text and a means of critical education in building political awareness among the public.

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