https://jjcit.org/paper/190
SEMANTIC RETRIEVAL FOR INDONESIAN QURAN AUTOCOMPLETION
10.5455/jjcit.71-1668279800
Rian Adam Rajagede,Kholid Haryono,Rizan Qardafil
Semantic retrieval,Quran auto-completion
910
155
7-Dec.-2022
1-Feb.-2023 and 22-Feb.-2023
5-Mar.-2023
Attending lectures is a common way to learn Islamic knowledge. The speaker talks in front of the forum and
participants take notes on the lecture material. Many participants listen to the lecture while taking notes either in
books or on other digital devices to avoid forgetting the discussed topics. However, note-taking during the lecture
can be challenging, with no complementing module from the speaker. Lecturers have different paces and varying
ways of delivering. In addition, sometimes, participants cannot always focus during the lecture. Those factors can
cause problems in the note-taking process: some details can be lost or even shift the meaning. For note-taking on
sensitive topics, such as verses from the Quran, the note-taking process must be done carefully and avoid mistakes.
In this study, we proposed an autocomplete system for the Indonesian translation of the Quran that will help the
user in note-taking in Islamic lectures. The user writes down words, the parts of the Quran verse that he/she hears
and the system will retrieve the most similar verses. With semantic retrieval, the user does not need to write down
the exact words of the verses he/she heard. The system can also handle typographical-errors that usually occur in
note-taking. We use FastText and calculate the cosine distance between the query and verses for the retrieval
process. We also performed several optimization steps to create a robust system for the production stage. The
system is evaluated by comparing how close the returned verse is with the ground truth. The proposed method's
result in terms of accuracy reached 70.59% for the top 5 retrieved verses and 76.47% for the top 10 retrieved
verses.