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COMPARING GOOGLE TRANSLATE AND DEEPL TRANSLATE IN TRANSLATING SHORT STORY SHERLY SAPUTRI 3302201020 ENGLISH EDUCATION DEPARTMENT FACULTY OF TEACHER TRAINING AND EDUCATION UNIVERSITAS BANTEN JAYA 2024
In the era of globalization, effective English communication is essential for accessing information and news in various fields. Nowadays, literary works such as English short stories are popular in all ages, and also used by teachers as learning materials. However, not all content in English books is easily understood by non-native speakers, necessitating the development of fast and productive translation tools . Recently, technology has made a significant impact on the translation industry, making it easier for people to understand and translate complex texts. Machine translation, also known as human-assisted translation, was developed to assist humans in the translation process. Regardless of its function, technology remains an important aspect in the current state of translation. Due to these factors, translation tools must be designed efficiently and effectively . The tool needed is machine translation. For example, Google Translate and DeepL offer solutions for translating text between languages. Background of the Problem
This research focuses on comparing between Google Translate and DeepL Translate . Sub-focus of the research is the translation results between Google Translate and DeepL Translate in translating figurative language in short story. How is the performance of machine translation between Google Translate and DeepL Translate when translating figurative language in short story by Charles Perrault from English to Indonesian? What types of figurative language are used in short story by Charles Perrault? Focus and Sub-focus Formulation
Objective of the Study To identify the performance of machine translation between Google Translate and DeepL Translate in translating figurative language in short story from English to Indonesian. To find out the types of figurative language in short story by Charles Perrault.
Relevant Research Halimah ( 2018) “Comparison of Human Translation with Google Translation of Imperative Sentence in Procedures Text” Parthama et al., (2021 ) “Translation Comparison on Manual Product and Machine Translation” Sipayung et al., (2021) “Comparison of Translation Techniques by Google Translate and U-Dictionary: How Differently Does Both Machine Translation Tools Perform in Translating?” This study investigated the similarity between human translation and machine translation to translate procedure text. The result shows that the equation between human translation and machine translation in translating procedural text is low. This research showed that both machines produce different target texts for the same source language due to different applications of techniques, with U-dictionary proven to apply more variety of translation techniques than Google Translate. The result shows that manual product and machine translations use different lexical word choices at the word and phrase levels.
T ranslation . Based on the definitions above, it can be concluded that translation commonly refers to the process of transferring messages from one language (source language) to another language (target language). According to Nida and Taber (2020), translation is the transfer of both meaning and message from the source language to the target language. T heoretical Framework Arono & Nadrah (2019), translation involves a conversion of the meaning of something from one language to another language. Larson (2019), translation can be defined as change of form.
Accuracy An accurate translation will preserve the original meaning and avoid mistakes or misunderstandings. A spect of Translation Quality Assessment Readability Readability is concerned with how naturally and easily a translation can be read by the target readers. Acceptability To produce an acceptable and natural-sounding translation, a translator should use the right techniques, use the right expression in the target language, and also adapt the culture to the target language.
Google Translate is a translation machine that uses a technique called Statistical Machine Translation (SMT). This means that the input data entered by the user is analyzed and recognized based on its sentence patterns ( Masdhalifa , 2021). M achine Translation Google Translate DeepL is an AI (Artificial Intelligence) based translation tool that is able to capture various variations and contexts that can be translated. DeepL
F igurative Language Figurative language is the utilization of language or words to express something without directly describing the original meaning in conveying something . Personification Metaphor Simile Hyperbole Onomatopoeia Repetition S hort Story According to Keegan, the definition of short story is a narrative. In general, short story consists of about 1.500 to 5.000 words that have a clear beginning, middle, and ending. Short story are able to be read in one-sitting, making it an ideal choice for busy readers who enjoy a complete story without reading a lengthy novel.
M ethodology of Research Research Design In t his research, the researcher use descriptive qualitative method. This research is a comparison research that compares the quality of Google Translate and DeepL Translate based on the quality aspect criteria as proposed by Nababan (2015). Setting of the Research The researcher collect the data at home and observe the data from the short story in front of computer while using internet access. As a result the researcher could find the data to analyze. This research starts from December 2023 until August 2024. Subject of the Research The subject of this research is a short story taken from the book The Tales of Mother Goose by Charles Perrault. Cinderella, or The Little Glass Slipper Sleeping Beauty in The Wood Little Thumb
T echnique of Collecting Data Reading the short story Identifying figurative language in the story Classifying the types of figurative language Translating figurative language from English to Indonesian Comparing the quality of translations from Google Translate and DeepL Translate by using an assessment rubric adapted from Nababan (2015). This research used data analysis techniques from Miles and Huberman , namely data reduction, data presentation, and drawing conclusions. T echnique Analysis Data