The new Toraja destination: adding value ‘Toraja coffee’ of the sustainable tourism development

Hasyim, Muhammad; Arafah, Burhanuddin; Kuswarini, P.

doi:10.1088/1755-1315/575/1/012072

Cited by 33 publications

(31 citation statements)

References 8 publications

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“…Qualitative research is an exploratory research (Arafah & Hasyim, 2019;Arafah & Hasyim, 2020). This provides insights into the problem or helps to develop ideas or hypotheses (Hasyim et al, 2020). In using this methodology, we explained the implicature phenomena which happen in the talk show by collecting the entire information in detail.…”

Section: Methodsmentioning

confidence: 99%

Conversational Implicatures on Saturday Night Live Talk Show

Yulianti

Arafah

Rofikah

et al. 2022

jltr

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Conversational implicature seems to be an everlasting concern in pragmatics for its wide-ranging investigation possibility. Applying Gricean’s principles, the present study examined the types of conversational implicatures found in the Saturday Night Live talk show. This research used a qualitative method with a pragmatic approach. The research data were collected from the utterances in Season 46 Episode 5 accessed from MBC's channel (www. saturday night live – NBC.COM). The result indicated that there were two types of conversational implicatures found in Saturday Night Live talk show namely: First, particularized conversational implicature, and second, generalized conversational implicature. We found that the utterances containing particularized implicature outnumbered the ones with generalized implicature. In our interpretation, the dominance of particularized implicature reflects the centrality of the particular context in producing and inferring utterances for meaningful and effective communication.

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Section: Methodsmentioning

confidence: 99%

Conversational Implicatures on Saturday Night Live Talk Show

Yulianti

Arafah

Rofikah

et al. 2022

jltr

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show abstract

“…The signifier is a physical record of information messages in certain media, signified is a concept generated by signified (physical records of information messages) and signs are the association of signifier and signified that is referred to as signification (Hasyim et al, 2021). Semiotically, the translation of MT is an activity of transferring signs (meaning) that occurs semiotically between the signifier and signified (Hasyim et al, 2020). In MT translation, a signifier is a physical record of information and signified is a concept (meaning) of a physical record of information.…”

Section: Figure 1 the Machine Translation Semiosis Processmentioning

confidence: 99%

Human-Robots And Google Translate: A Case Study Of Translation Accuracy In Translating French-Indonesian Culinary Texts

Hasyim¹

2021

TURCOMAT

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Google Translate (GT) is the most widely used translator application in the world. The function of GT is not merely as tools but has become a means in personal communication, learning and business matters. This paper aims to examine the GT accuracy in translating culinary texts. This paper used a semiotic approach to analyze the equivalence of GT from the source language to the target language. The data source as the object of study is French culinary texts retrieved from the internet. It can be concluded that the accuracy of GT in culinary texts lies in words, phrases and sentences. GT does not yet have good accuracy in translating culture between source and target languages. In the semiotic perspective, the GT translation model is the translation from the source language form to the target language form (signifier) by maintaining the concept (meaning). GT does not yet have a translation feature in a cultural context. The advancement in information technology shows that GT, as a human-robot, can replace human translators.

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“…A signifier is a physical record of an information message in certain media and a signified is a concept generated by a signifier (a physical record of an information message). The semiotic translation is a significant transfer activity [21]. MT is an activity of transferring signs (verbal language).…”

Section: Machine Translation and Semioticsmentioning

confidence: 99%

Artificial Intelligence: Machine Translation Accuracy in Translating French-Indonesian Culinary Texts

Hasyim¹,

Saleh²,

Yusuf³

et al. 2021

IJACSA

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The use of machine translation as artificial intelligence (AI) keeps increasing and the world's most popular translation tool is Google Translate (GT). This tool is not merely used for the benefits of learning and obtaining information from foreign languages through translation but has also been used as a medium of interaction and communication in hospitals, airports and shopping centers. This paper aims to explore machine translation accuracy in translating French-Indonesian culinary texts (recipes). The samples of culinary text were taken from the internet. The research results show that the semiotic model of machine translation in GT is the translation from the signifier (forms) of the source language to the signifier (forms) of the target language by emphasizing the equivalence of the concept (signified) of the source language and the target language. GT aids to translate the existing French-Indonesian culinary text concepts through words, phrases and sentences. A problem encountered in machine translation for culinary texts is a cultural equivalence. GT machine translation cannot accurately identify the cultural context of the source language and the target language, so the results are in the form of a literal translation. However, the accuracy of GT can be improved by refining the translation of cultural equivalents through words, phrases and sentences from one language to another.

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The new Toraja destination: adding value ‘Toraja coffee’ of the sustainable tourism development

Cited by 33 publications

References 8 publications

Conversational Implicatures on Saturday Night Live Talk Show

Conversational Implicatures on Saturday Night Live Talk Show

Human-Robots And Google Translate: A Case Study Of Translation Accuracy In Translating French-Indonesian Culinary Texts

Artificial Intelligence: Machine Translation Accuracy in Translating French-Indonesian Culinary Texts

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