ANALISIS METODE LATENT DIRICHLET ALLOCATION (LDA) UNTUK CLUSTERING TERJEMAHAN AL-QUR’AN BAHASA INDONESIA

Ahmad Septiawan, NIM.: 16650053 (2020) ANALISIS METODE LATENT DIRICHLET ALLOCATION (LDA) UNTUK CLUSTERING TERJEMAHAN AL-QUR’AN BAHASA INDONESIA. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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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".

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