Analisis Kesalahan Morfologi Penerjemahan Arab–Indonesia pada ChatGPT

Ruhmadi, Abdul; Al Farisi, Mohamad Zaka

doi:10.37680/aphorisme.v4i1.3148

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…In morphosemantics, in addition to changing word building, it also has implications for changes in meaning resulting from morphological processes (Ghozali & Khoiriyatunnisa, 2021). Morphology according to Al-Ghalayaini is defined as the study of basic words to gain an understanding of the various forms of words in Arabic, including changes and development of these words (Nurmala et al, 2022;Ruhmadi & Al Farisi, 2023). There are several aspects of morphology including shighat (Miftahuddin, 2015), wazan-wazan tashrif (patterns) (Irawan, 2020), a collection of word forms consisting of archetypes, lawashiq (affixes) (Ghalayain in Asy'ari, 2016), and zawaid (Hassan in Kosim, 2020), and word derivation (Anis in Ruslan et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Morphosemantic Analysis Skills Among Digital Native Non-Arabic Speakers

Adawiah,

Sopian,

Nursyamsiah

2024

ukazh

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This study aims to determine the ability of Arabic morphosemantic analysis on digital natives who are not native Arabic speakers. The research method used is descriptive quantitative with percentage-based analysis. Data were collected through an open-ended multiple-choice test that was structured based on morphosemantic indicators. The validity of the test instrument has been confirmed by Arabic morphosemantics experts. The study involved 34 digital native Arabic students, selected through a purposive sampling technique. The results show that the morphosemantic analysis ability of digital native non-Arabic speakers is generally considered insufficient, with a percentage of 49.2%. The ability in the aspects of word derivation and shighat is classified as moderate with a percentage of 67.6% and 65.7% respectively, while the ability in the aspects of affixes, word roots, semantic aspects, and wazan is categorized as "very poor" with a percentage of 44.7%, 42.1%, 37.2%, and 36.4% respectively. This study concludes that the morphosemantic analysis ability of digital native non-Arabic speakers is still at a deficient level, and effective strategies need to be developed to improve it Penelitian ini bertujuan untuk mengetahui kemampuan analisis morfosemantik bahasa Arab pada digital native yang bukan penutur asli Arab. Metode penelitian yang digunakan adalah deskriptif kuantitatif dengan analisis berbasis persentase. Data dikumpulkan melalui uji pilihan ganda terbuka yang disusun berdasarkan indikator morfosemantik. Validitas instrumen uji telah dikonfirmasi oleh ahli morfosemantik bahasa Arab. Penelitian melibatkan 34 mahasiswa bahasa Arab digital native, dipilih melalui teknik purposive sampling. Hasilnya menunjukkan bahwa kemampuan analisis morfosemantik digital native non penutur Arab secara umum tergolong kurang, dengan persentase 49.2%. Kemampuan dalam aspek derivasi kata dan shighat diklasifikasikan sebagai sedang dengan persentase masing-masing 67.6% dan 65.7%, sementara kemampuan dalam aspek imbuhan, akar kata, aspek semantik, dan wazan dikategorikan sebagai “sangat kurang” dengan persentase masing-masing 44.7%, 42.1%, 37.2%, dan 36.4%. Kesimpulan dari penelitian ini adalah bahwa kemampuan analisis morfosemantik digital native non penutur Arab masih memiliki tingkat yang kurang, dan strategi yang efektif perlu dikembangkan untuk meningkatkannya.

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Section: Introductionmentioning

confidence: 99%

Morphosemantic Analysis Skills Among Digital Native Non-Arabic Speakers

Adawiah,

Sopian,

Nursyamsiah

2024

ukazh

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“…By providing real-time feedback, analyzing learners' individual needs, and offering customized learning materials, this technology has the potential to be a catalyst for the development of critical and analytical thinking skills. AI has also been used in translation, for example (Ruhmadi et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Transformation Of Learner Learning: Improving Reasoning Skills Through Artificial Intelligence (AI)

Susanto,

Andrianingsih,

Sutawan

et al. 2023

JoERI

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The research aims to explore the potential of artificial intelligence (AI) in the context of Learning as a solution to the challenges in developing thinking skills. Through the use of AI technology, the study focuses on finding out the relationship between AI and students' ability to argue. The approach used in this research is quantitative with correlational methods. Research results show that AI in learning can be a significant catalyst for the development of critical and analytical thinking skills. Students engaged in learning environments using AI showed an improvement in problem-solving, information-analysis, and critical thinking skills so that their ability to argue improved. In addition, learning efficiency, and motivates the learners to reach their maximum potential. This research contributes to an understanding of how AI in education can have a positive impact on the development of argumentation skills. The practical implications of these findings can open the door to the development of more effective and personalized learning strategies in the future, creating a responsive and adaptive educational environment.

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Analisis Kesalahan Morfologi Penerjemahan Arab–Indonesia pada ChatGPT

Cited by 2 publications

References 8 publications

Morphosemantic Analysis Skills Among Digital Native Non-Arabic Speakers

Morphosemantic Analysis Skills Among Digital Native Non-Arabic Speakers

Transformation Of Learner Learning: Improving Reasoning Skills Through Artificial Intelligence (AI)

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