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All Journal Excellence Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Fahri Afandi
Universitas Sumatera Utara
Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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When Sadness Speaks in Two Tongues: English Code-Switching in Indonesian TikTok Sad Post Captions Fahri Afandi; Kanaya Nur Nabila; Shalsa Billa Meisya; Rahmadsyah Rangkuti
EXCELLENCE: Journal of English and English Education Vol 5 No 2 (2025): EXCELLENCE
Publisher : English Education Study Program FKIP Universitas Alwashliyah Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47662/ejeee.v5i2.1342

Abstract

This study investigates the phenomenon of code-switching in sad post captions on TikTok, focusing on its types, contexts, and communicative functions. Although code switching on digital platforms has been widely studied, the switching context remains underexplored. Using a qualitative descriptive approach with content analysis, this study examines 29 sad-related TikTok captions collected between 2022 and 2025. The analysis is guided by Muysken’s typology, Blom and Gumperz’s contextual categorization, and the functional frameworks of Gumperz and Wardhaugh. The findings show that alternation is the most frequent type of code-switching, indicating frequent shifts between Indonesian and English across clauses. Contextually, metaphorical switching predominates, suggesting that code switching is strongly driven by emotional emphasis. Functionally, message qualification and affective expression dominate, indicating that code switching primarily serves to intensify emotional meaning rather than to convey informational content. This study contributes to sociolinguistics, highlighting how Indonesian bilingual users perform code-switching on TikTok as a strategic medium for emotional expression, identity performance, and digital self-representation.
Optimizing Listening Skills with the ZIELTS Application: A Technological Approach to IELTS Success Desri Maria Sumbayak; Alemina Br. Perangin-Angin; Vivi Adryani Nasution; Indah Putri Tamala; Rosalyn Magdalena Br. Sitorus; Fahri Afandi; Rania Alifiandra Matulatuwa
JENTIK : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 5 No. 1 (2026): Forthcoming Issue
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/6tp1t330

Abstract

Purpose of the Study: 1. To assess how the ZIELTS application affects candidates' performance in the IELTS listening section; 2. To identify key features of the ZIELTS application that enhance listening skills; 3. To evaluate candidates' perceptions of the ZIELTS application's effectiveness.Methodology: A mixed-methods design was adopted combining quantitative performance measurement and qualitative feedback analysis. Ten participants were randomly selected and assessed using pre- and post-tests to evaluate listening competence. Participants practiced listening using the ZIELTS application for two weeks and recorded the frequency of use and topic preferences in logbooks. Quantitative data were analyzed using descriptive statistics and a paired-samples t-test in SPSS, while qualitative data from logbooks and Focus Group Discussions (FGD) were examined through thematic analysis.Main Findings: The outcome revealed a modest but not statistically significant improvement in listening skills: the mean band score increased from 6.75 to 6.90, t(9) = 0.896, p = 0.394. While there was some variation in the progress of individual participants, users were satisfied with the user-friendly design of the application, the wide range of topics, and additional features like vocabulary lists and audio transcripts. Minor technical issues (e.g., inconsistencies between audio and text) were identified as opportunities for future enhancements.Novelty/Originality of This Study: This study is one of the earliest empirical explorations of ZIELTS, a purpose-built, exam-aligned AI application launched in March 2024, as a means of improving IELTS listening performance. Using a mixed-methods approach that integrates quantitative pre- and post-test assessments with qualitative learner feedback, it contributes original, context-specific evidence to the relatively under-researched intersection of AI-assisted language learning and IELTS listening preparation.
Co-Authors Alemina Alemina Desri Maria Sumbayak Indah Putri Tamala Kanaya Nur Nabila Rahmadsyah Rangkuti Rania Alifiandra Matulatuwa Rosalyn Magdalena Br. Sitorus Shalsa Billa Meisya