Muhammad Arif Rahman
Universitas Islam Negeri Siber Syekh Nurjati Cirebon

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CONSTRUCTING CULTURAL IDENTITY IN BIPA PEDAGOGY: A CRITICAL DISCOURSE ANALYSIS OF AR-ENHANCED LEARNING MATERIALS BASED ON CIREBON HERITAGE Indrya Mulyaningsih; Tiwaporn Jan-Kaew; Tatik Ekawati; Veni Nurpadillah; Muhammad Arif Rahman
Jurnal Gramatika Vol 12, No 1 (2026): Spring Issue (April-September)
Publisher : Universitas PGRI Sumatera Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22202/jg.2026.v12i1.10607

Abstract

Integrating cultural identity into Bahasa Indonesia for Penutur Asing (BIPA) pedagogy often prioritizes content enrichment over discursive and ideological engagement. This study addresses this limitation by investigating how cultural identity is constructed, depicted, and facilitated through Augmented Reality (AR) within learning materials derived from Cirebon's historical landscape for Thai learners. Employing Critical Discourse Analysis (CDA) alongside design-based research, the study scrutinizes pedagogical designs, instructional texts, visual representations, and learner feedback to identify prevailing identity discourses embedded within the technology. Results indicate that Cirebon's cultural identity is predominantly portrayed through harmonious, heritage-focused narratives emphasizing tradition and historical continuity, whereas alternative or contested identities receive minimal representation. Consequently, AR functions not merely as a technological tool but also as a semiotic mechanism that validates specific cultural interpretations and pedagogical ideologies. The study argues that AR-enhanced BIPA materials function as ideological frameworks that significantly shape learners' perceptions of Indonesian cultural identity. This research advances BIPA instruction beyond technological augmentation, advocating for critical language education that acknowledges technology's role in perpetuating cultural ideologies. It highlights the need for educators to examine how digital media shapes learners' cultural understanding critically. Future research should conduct comparative discourse analyses across diverse cultural contexts and investigate learner resistance and reinterpretation of these narratives. By examining the intersection of technology, discourse, and identity, this study contributes to a nuanced understanding of digital pedagogy in language learning, ensuring that cultural representation remains critical rather than superficial.
Gejala Hiperkorek Pada Tugas Mahasiswa yang Berbantukan Artificial Intelligence: Kajian Semantik Emah Khuzaemah; Veni Nurpadillah; Dima Azharul Fahira; Muhammad Arif Rahman
Ranah: Jurnal Kajian Bahasa Vol 14, No 2 (2025): Ranah: jurnal Kajian Bahasa
Publisher : Badan Pengembangan dan Pembinaan Bahasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26499/rnh.v14i2.8019

Abstract

This study aims to analyze the phenomenon of hypercorrection in students’ assignments written with the assistance of Artificial Intelligence (AI). This research employs a descriptive qualitative method. The data sources are students’ assignments produced with the help of AI applications. The data consist of text excerpts in the form of words, phrases, clauses, and sentences containing semantic errors. The research was conducted at three universities UIN Siber Syekh Nurjati, Universitas Kuningan, and Universitas Darul Ma’arif specifically in the Indonesian Language Education study program, from May to September 2024. Data were collected using the non-participatory observation method (Simak Bebas Libat Cakap) and note-taking technique. The data were analyzed using the distributional method (Metode Agih) with the Immediate Constituent Analysis technique (Bagi Unsur Langsung/BUL) and the translational equivalent method (Metode Padan Translasional) with the Determining Element Sorting technique (Pilah Unsur Penentu/PUP). The results show that from the essays produced by students across the three universities in the Ciayumajakuning region, there were 27 cases of hypercorrection errors. These errors generally occurred due to the excessive or inappropriate use of words that altered the intended meaning. Overall, the findings indicate that the use of AI in academic writing has not yet fully supported linguistic accuracy, particularly in the semantic aspect. Abstrak Penelitian ini bertujuan untuk menganalisis gejala hiperkorek dalam tugas mahasiswa yang disusun dengan bantuan kecerdasan buatan (Artificial Intelligence/AI). Penelitian ini menggunakan metode deskriptif kualitatif. Sumber data penelitian berasal dari tugas mahasiswa yang menggunakan bantuan AI dalam proses penulisannya. Data penelitian berupa penggalan teks berupa kata, frasa, klausa, dan kalimat yang mengandung kesalahan berbahasa pada tataran semantik. Penelitian dilaksanakan di tiga universitas, yaitu UIN Siber Syekh Nurjati, Universitas Kuningan, dan Universitas Darul Ma’arif pada program studi Pendidikan Bahasa Indonesia, dengan waktu pelaksanaan mulai Mei hingga September 2024. Teknik pengumpulan data menggunakan metode simak bebas libat cakap (SBLC) dan teknik catat, sedangkan analisis data dilakukan dengan metode agih menggunakan teknik Bagi Unsur Langsung (BUL) serta metode padan translasional dengan teknik Pilah Unsur Penentu (PUP). Hasil penelitian menunjukkan bahwa dari teks esai mahasiswa di tiga universitas wilayah Ciayumajakuning ditemukan sebanyak 27 data kesalahan berbahasa yang tergolong gejala hiperkorek. Kesalahan tersebut umumnya berupa penggunaan kata berlebihan atau bentuk koreksi berlebihan yang tidak sesuai dengan konteks makna. Temuan ini menunjukkan bahwa penggunaan kecerdasan buatan dalam penyusunan tugas akademik belum sepenuhnya mampu mendukung ketepatan berbahasa, khususnya pada aspek semantik.