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All Journal Fon: Jurnal Pendidikan Bahasa dan Sastra Indonesia
Tommi Yuniawan
Program Studi Ilmu Pendidikan Bahasa S3 Universitas Negeri Semarang
Education Languange, Linguistic, Communication & Media Other

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KAJIAN SEMANTIK PADA FENOMENA PLEONASME DALAM ESAI MAHASISWA BERBANTUKAN ARTIFICIAL INTELLIGENCE Veni Nurpadillah; Emah Khuzaemah; Tommi Yuniawan; Arief Darmawan
Fon: Jurnal Pendidikan Bahasa dan Sastra Indonesia Vol 21 No 2 (2025)
Publisher : Pendidikan Bahasa dan Sastra Indonesia Universitas Kuningan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/sckc0957

Abstract

This study aims to identify and analyze pleonasm phenomena in student essays written with the assistance of Artificial Intelligence (AI) from a semantic perspective. Pleonasm is defined as the excessive use of linguistic elements that results in semantic redundancy and inefficiency in academic communication. The research employs a qualitative descriptive method with data consisting of segments of words, phrases, clauses, and sentences from student essays found to contain semantic errors. Data were collected from students at three universities in the Ciayumajakuning region: UIN Siber Syekh Nurjati, Universitas Kuningan, and Universitas Darul Ma’arif, all of whom used AI applications such as ChatGPT, Samwell.ai, and ShortlyAI in their writing tasks. Data collection techniques included non-participatory observation and note-taking, while analysis was conducted using the distributional method (Immediate Constituent technique) and the translational identity method (Determining Element Sorting technique). Analysis of 37 data samples revealed various forms of pleonasm, such as phrase repetition, unnecessary use of clitics or grammatical elements, and semantic redundancy within sentence structures. The findings show that students tend to accept AI-generated texts uncritically, without revising them for semantic clarity. Moreover, AI has not yet demonstrated the ability to produce semantically economical texts, partly due to biases in its training data. This study recommends integrating semantic literacy into technology-based academic instruction and developing AI features that promote greater semantic efficiency. These findings also support the need for academic policies that guide the ethical and reflective use of AI in higher education.
Co-Authors Arief Darmawan Emah Khuzaemah Veni Nurpadillah