SIAKAD machine learning for correcting errors in speaking Arabic

Abstract

Information technology provides a lot of convenience for humans in completing their tasks and getting results according to targets. In line with that, language teachers have a duty to find out the level of language skills and forms of language errors in students. Machine Learning as part of technology can be maximized to detect forms of Arabic speaking error in students. This study was conducted with a qualitative approach. Data were collected via SIAKAD machine learning containing Arabic videos. Based on the results, the SIAKAD machine learning uncovered several Arabic speaking errors such as grammar, pronunciation, shifat al-huruf, vowels, word expression, and concatenated sentences. Therefore, machine learning with various types can be maximized in Arabic learning which ultimately leads to technological developments that must be accompanied by the ability of teachers to be skilled in operationalizing technology. 

Keywords: Arabic Language Education; Machine Learning; Speaking Errors