Developing Multilingual Automotive E-Dictionary Based on Corpus Linguistics
- DOI
- 10.2991/assehr.k.220301.160How to use a DOI?
- Keywords
- automotive; E-dictionary; multilingual; corpus; linguistics
- Abstract
The study aims to compile a dictionary that makes students, Indonesian workers, especially mechanics, supervisors in the automotive sector who work or study in the automotive industry need an automotive dictionary. The Design and Development (D&D) model used in this study and obtaining data were taken from automotive books, automotive websites for technical language specifically for automotive engines, and additional data from official and non-official workshops for workshop languages commonly used daily. This study used the Antconc application for automotive text corpus processing. This dictionary has three languages: English (automotive)-Indonesian-local (workshop language), which were arranged to start from the first alphabet that is pronounced. The product’s result was a multilingual automotive e-dictionary which several experts validate categorized as a “valid and excellent” product. The multilingual automotive e-dictionary group in the experimental group was more effective; 0.6 than the control group; 0.3. The Cronbach Alpha value from six variables: content, format, accuracy, ease of use, timeliness and user satisfaction was greater than 0.60 (the existing provisions). It showed that the variable was “reliable” as a measure of reliability test.
- Copyright
- © 2022 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Yuliana Ningsih AU - Adhiela Noer Syaief AU - Kurnia Dwi Artika AU - Herpendi Herpendi PY - 2022 DA - 2022/03/04 TI - Developing Multilingual Automotive E-Dictionary Based on Corpus Linguistics BT - Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021) PB - Atlantis Press SP - 962 EP - 967 SN - 2352-5398 UR - https://doi.org/10.2991/assehr.k.220301.160 DO - 10.2991/assehr.k.220301.160 ID - Ningsih2022 ER -