@phdthesis{digilib42685,
           month = {July},
           title = {ANALISIS METODE LATENT DIRICHLET ALLOCATION (LDA) UNTUK  CLUSTERING TERJEMAHAN AL-QUR?AN BAHASA INDONESIA},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.: 16650053 Ahmad Septiawan},
            year = {2020},
            note = {Pembimbing : Muhammad Didik Rohmad Wahyudi, S.T., M.T.},
        keywords = {Text Mining, Laten Dirichlet Allocation (LDA), Terjemah Al-Qur'an},
             url = {https://digilib.uin-suka.ac.id/id/eprint/42685/},
        abstract = {Al-Qur'an is the Muslim holy book which is the main source of law in the 
life of the people. In understanding the Koran, it is necessary to be careful, because
the discussion of a theme may not necessarily be collected in one letter, sometimes
separate from other letters. Therefore research is carried out to find themes from all
verses of the Koran. 
This study aims to find hidden topics in the Indonesian Koran text 
translation data collection automatically in order to make it easier to understand the
verses of Al-Qura'an in full using the topic modeling method that is Latent Dirichlet
Allocation (LDA). The method is to extract 6236 verses from the Koran and then
find the best model topic outputs. 
Cv coherence measurement evaluation results give a value of 0.489256 with 
a model output of 7 topics, including "hubungan manusia dengan Allah",
"kekuasaan Allah", "ciptaan Allah", "cobaan dan ujian manusia", "ancaman
Allah", "firman Allah", "Nur".}
}