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All Journal Indonesian Journal of Applied Linguistics (IJAL) Jurnal Dimensi Pendidikan dan Pembelajaran JOURNAL OF APPLIED LINGUISTICS AND LITERATURE JURIKOM (Jurnal Riset Komputer) Jurnal JOEPALLT (Journal of English Pedagogy, Linguistics, Literature, and Teaching) Building of Informatics, Technology and Science Jurnal Bahasa Inggris Terapan
Meilinda, Lina
Politeknik Negeri Bandung
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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Verbal-visual evaluation in disaster reporting: Attitudinal analyses of hard news in The Jakarta Post Lina Meilinda; Yulizar Komarawan; Ilma Yullinda Rahmah
JOALL (Journal of Applied Linguistics and Literature) Vol. 11 No. 1 (2026): February, 2026
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/joall.v11i1.45946

Abstract

Disaster news helps the public understand crisis events and is often presented as factual and objective because it is classified as hard news. However, disaster reports still contain evaluative elements that shape how events and social actors are perceived. These evaluations are expressed through both language and news photographs. This study examines how such evaluations are constructed in disaster reports from The Jakarta Post through the interaction between written texts and accompanying news photographs. The study draws on Appraisal Theory, as proposed by White and Martin within the framework of Systemic Functional Linguistics. It analyses attitude resources in hard news reporting, realised through verbal and visual modes, and their intersemiotic relations. The data consist of eleven hard news articles, purposively selected 2024 that meet specific inclusion criteria. Both verbal and visual elements were analysed qualitatively, supported by simple frequency counts to identify dominant patterns. The findings show 185 attitude instances distributed across three types. Negative appreciation is the most common (47%), followed by negative affect (24%). These are mainly used to show physical damage, loss, and human suffering. In contrast, positive judgment makes up 29% of the data and is used to present institutions, such as government agencies and rescue teams, as responsible and effective. News photographs support these meanings by showing destruction, danger, and humanitarian action. Across text and images, repetition and extension strengthen evaluative meanings and create a consistent viewpoint. The study shows that disaster reporting is evaluative and multimodal. Therefore, it highlights the need for critical multimodal literacy to understand how meaning is constructed through language and images.
An Analysis of Grammatical and Mechanical Errors in LIUNIC ON THINGS Products’ Captions Muhamad Julio Moreno; Lina Meilinda; Krisna Yudha Bakhti
Jurnal Bahasa Inggris Terapan Vol. 9 No. 2 (2023): October 2023
Publisher : Jurusan Bahasa Inggris - Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/jbit.v9i2.4905

Abstract

Captions are one of the most important elements in social media because they can provide context, add narrative, or engage the audience with the posted content. However, captions that are written sometimes do not comply with the language rules used. This study aims to identify the kinds of grammatical and mechanical errors found in the captions of LIUNIC ON THINGS. The study applied theories from Dulay, Burt, and Krashen's Surface Strategy Taxonomy (1982), Warriner and Griffith (1973), and Mitchell (2008). This study applied a document analysis method; the process of reviewing and evaluating documents both printed and electronic in a methodical manner. This method is useful for identifying, categorizing, analyzing, and interpreting physical data to gain understanding and come to a conclusion. From the data collection, it was found that there were 66 grammatical errors and 61 mechanical errors in 50 captions released in 2022. The results revealed that the grammatical errors found in its Instagram caption are 63.6% of misformation, 25.8% of omission, 6.1% of misordering, and 4.5% of addition. The mechanical errors, on the other hand, are 50.8% of punctuation errors, 44.3% of capitalization errors, and 4.9% of spelling errors. It can be seen that misformation is the most common grammatical error, and punctuation is the most common mechanical error. The result of this study is expected to be used as one of the evaluation resources for social media caption makers to prevent the occurrence of grammatical and mechanical errors.
Co-Authors Carolina Magdalena Lasambouw Dewiyanti, Sri Diana Az Zahra Mahesti Djoni Djatnika Farida Agoes Krisna Yudha Bakhti Maisevli Harika Marsya Aprila Tayibnapis Muhamad Julio Moreno Muhammad Rizqi Sholahuddin Muhammad Yazidus Syukri Nuryati, Neneng Putri Dwi Maharani Rahmah, Ilma Yullinda Saudin, Saudin Setiadi Rachmat Sri Nur Yuliyawati Sulyaningsih, Iis Yessy Purnamasari Yuliah, Siti Yulizar Komarawan