Machine Learning - Google Translate: A Study of the Use of Machines in Translating Arabic Texts

Abstrak

The internalization of technology in Arabic language learning provides many conveniences for teachers and students in completing their assignments and achieving maximum results. However, you must remain alert to errors that appear in machine searches for Arabic vocabulary. Today, Google Translate (GT) machine learning (ML) can be used to search for Arabic vocabulary and at the same time, be alert to errors. This research used a qualitative approach with a case study method for 100 students at the As-Syifa Subang Al-Qur’an Science College, semester 3. The results of this research explain students’ perceptions of ML, where they expect ML to be an interactive medium in learning Arabic. Other results show that in using ML-GT there are several linguistic errors such as grammar, word order, phonetics, morphology, syntax, and semantics. Therefore, Arabic teachers and students can utilize ML in various ways in learning as an adaptive response to current developments. At the same time, teachers must improve their skills in applying learning technology.