Text mining for Indonesian translation of the Quran: A systematic review

Nowadays there is an increasing trend in computers used for learning Islamic knowledge from Indonesian Translation of AL-Quran (ITQ). As a result, substantial knowledge is stored in the form of unstructured text on chapters (surah) of ITQ. Text mining is exciting research area incorporated with information extraction, natural language processing, information retrieval, and data mining. It tries to discover knowledge from unstructured text. Text mining on ITQ is an ability to process Indonesian text into sentences (ayat) or documents (surah), interpret its text meaningfully, and identify as well as extract relationship among concept to directly answer the question of interest. This paper presents a review of concepts, searching and question answer (SQA) applications, and issues on text mining for ITQ. We reviewed the research papers highlighted some of the problems, gaps, critical challenges in this area and proposed some future research directions. Review method is composed of three phases: planning, conducting, and reporting the review. The results of this review show most existing research on text mining based on complexity, ambiguities, and optimizing for the SQA system application. Finally, this review can be beneficial to researchers in this area to carry it to the next level.