HARYANTO, Nadya Salsabila (2023) Analysis of Loss and Gain and Translation Accuracy in the Translation of Siren's Lament Webtoon by Instantmiso. Skripsi thesis, Universitas Jenderal Soedirman.

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Abstract

The research entitled “Analysis of Loss and Gain and Translation Accuracy in the Translation of Siren’s Lament Webtoon by Instantmiso” aims to analyze the types of loss and gain and the effect of loss and gain on its translation accuracy. This study uses Bassnett’s loss and gain theory as well as Nababan’s translation quality assessment theory (2012) to analyze the accuracy. This study employs qualitative descriptive method in which the collected data is analyzed and described in order to identify the types of loss and gain and the translation’s accuracy. Total sampling is used as the sampling technique. The results of the analysis show that there are 75 data of loss and gain, with 45 data affected by loss and 30 data affected by gain. Loss and gain occur at four different levels: word loss/gain, phrase loss/gain, clause loss/gain, and sentence loss/gain. From 45 data of loss, 27% is word loss, 40% is phrase loss, 20% is clause loss, and 13% is sentence loss. The gain discovered, on the other hand, is 40% word gain, 40% phrase gain, 13% clause gain, and 10% sentence gain. The accuracy of the translation is also affected by loss and gain. Data employing loss that is classified as accurate is 47%, 42% is classified as less accurate, and 11% is classified as inaccurate. Furthermore, the data employing gain that is classified as accurate is 97%, while the remaining 3% is belong to inaccurate. There is no inaccurate data found in the data affected by gain. Overall, the translation of the data influenced by loss and gain in the Siren’s Lament might not be perfect but it is still categorized as accurate. It is proved by the result of the questionnaire that shows a high level of translation accuracy from both loss and gain data with a total 67% belong to accurate, 27% belong to less accurate, and 6% belong to inaccurate.

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