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ANALYSIS OF CATEGORY SHIFT ON EMMA HEESTERS’S COVER SONG LYRICS ON YOUTUBE Wisnu Sanjaya; Baharuddin; Lalu Jaswadi Putera; Lalu Ali Wardana
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 10 No. 1 (2024): Volume 10 No. 01 Maret 2024
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v10i1.2557

Abstract

This study aims to Analyze and Categorize the types of category shift that occur in the English version of Indonesian Song’s lyrics covered by Emma Heesters. This research was Descriptive Qualitative Research method and catford (1965)translation shift theory was caried in this study . The sample of this research was the lyrics which consist in 7 Song’s of her cover . The data of the research take on Emma Heester Youtube chanel. Based on Catford''s theory as the foundation of this reserach , a total of 189 data were found across 7 songs covered by Emma Heesters. Structure Shift emerged significantly, reaching 20.11%, Intra System Shift, representing 15.87%, . Conversely, Class Shift appeared with the lowest frequency at 5.82%,. Among the identified categories, Unit Shift emerged as the most dominant, constituting 58.20% of the dataset. In summary, the study revealed the dominance of Unit Shift in these translations, highlighting the complexity of adapting song lyrics, including stylistic, rhyme and rhythm consideration and emotional elements, to a different language.
A COMPARATIVE ANALYSIS OF ACCURACY BETWEEN GOOGLE TRANSLATE AND BARD IN TRANSLATING ABSTRACTS OF SCIENTIFIC JOURNALS Rosita; Baharuddin; Lalu Jaswadi Putera; I Made Sujana
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 10 No. 1 (2024): Volume 10 No. 01 Maret 2024
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v10i1.2558

Abstract

The accuracy of machine translation is still debatable. This study aims to compare the accuracy of Google Translate and BARD in translating scientific journal abstracts. The main focus is to evaluate the extent to which these two platforms can preserve the meaning and quality of translation in the abstracts of Adabiyyāt journal. This research utilizes two main theories in evaluating translation accuracy, namely the Human-mediated Translation Edit Rate (HTER) by Snover (2006) to identify translation errors and the Translation Quality Index (TQI) by Schiaffino & Zearo (2005) to calculate overall accuracy results. The results of this study show that GT is proven to be more accurate than Bard in translating abstracts from scientific journals. This can be seen from the final result of GT's calculation, which is greater than that of Bard. GT scored 99,7% in total, while Bard only got 99,1%.
The ACCURACY OF CHATGPT IN TRANSLATING LINGUISTICS TEXT IN SCIENTIFIC JOURNALS Rusmita Aeni; Baharuddin; Lalu Jaswadi Putera; Boniesta Zulandha Melani
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 10 No. 1 (2024): Volume 10 No. 01 Maret 2024
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v10i1.2559

Abstract

AI-generated Machine Translation, such as Neural Machine Translation, has transformed the traditional role of translators. There is a viral language model called ChatGPT. ChatGPT is a conversational variation of Natural Language Processing (NLP) Generative Pretrained Transformer (GPT) models. This research is aimed to analyze the accuracy of ChatGPT in translating linguistics text in scientific journals. The study employs qualitative approach and includes into quality assessment. The data source of this research is a National journal named Ranah focused on language and linguistics, Volume 12 number 1-13 published in 2023. The data then analyzed using few theories. Snover’s theory in finding error rate (HTER) and translation quality index to measure translation accuracy by Schiaffino and Zearo. Through the analyzation result, it could be inferred that In the analysis of 2034 words using Snover's theory revealed specific categories of translation errors in ChatGPT with a total of 6% error, there were 15 insertion errors (0.73%), 22 deletion errors (1.32%), 72 substitution errors (3.54%), and 17 shifting errors (0.83%). With a total error of 6,4%, this brings ChatGPT's accuracy rate in translating linguistic scientific texts to 93,6%. Through the analyzation result, it could be inferred that ChatGPT successfully translates scientific text within excellent category .
TRANSLATION QUALITY OF LITERARY TEXT OF INDONESIAN NOVEL “LASKAR PELANGI (THE RAINBOW TROOPS)” INTO ENGLISH USING FACEBOOK MACHINE TRANSLATION Yeni Yulianti; Baharuddin; Lalu Jaswadi Putera; Udin
Didaktik : Jurnal Ilmiah PGSD STKIP Subang Vol. 10 No. 1 (2024): Volume 10 No. 01 Maret 2024
Publisher : STKIP Subang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36989/didaktik.v10i1.2719

Abstract

This study aims to identify and categorize the types of corrections or post-edits found in English translations of Indonesian literary texts and evaluate the quality of these translations using Facebook Machine Translation (FMT), specifically in translating the Indonesian novel "Laskar Pelangi" into English. Using a descriptive qualitative approach, the study examines various types of translation post-edits such as substitutions, insertions, deletions, and shifts, identified through HTER analysis. The correction or post-editing process for the English translation of the novel "Laskar Pelangi" using FMT was scrutinized and categorized by comparing the original Indonesian source text with the post-edited version translated by a professional human translator and by referring to the translated book version in English. The findings indicate that FMT achieves an 80.23% translation accuracy rating at the Good level based on TQI, with a 19.77% rate of translation inaccuracy. The types of post-edits identified include 54.2% substitutions, 33.7% insertions, 7.2% deletions, and 4.8% shifts. The analysis highlights a prevalent occurrence of substituted words in the post-edited translation, showcasing FMT's commendable performance in addressing translation issues on the popular platform.
Co-Authors Boniesta Zulandha Melani Dwi Tanti Vidina Sunu Sunu I Made Sujana Lalu Ali Wardana Putera, Lalu Jaswadi Rosita Rusmita Aeni Udin Untung Waluyo Wisnu Sanjaya Yeni Yulianti