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November 29, 2023
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November 30, 2023
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Abstract

Dalam aksara arab semua kata eksplisit bisa dibaca, sedangkan dalam dialek arab bukan
tata bahasa yang mudah untuk dimengerti. Sekalipun seseorang dapat menghafal banyak
kosakata bahasa Arab (mufradat/vocabulary) atau menguasai ilmu Tasrif (bukan hanya
teori), tidak ada jaminan seseorang tersebut akan bisa membaca dengan tepat teks
gundul. Automatic Quranic Arabic ini merupakan suatu inovasi dalam bidang Deep
Learning yang memberikan output berupa harakat secara otomatis untuk teks bahasa Arab
gundul. Penelitian sebelumnya menggunakan Long Short-Term Memory (LSTM) untuk
memperbaiki kesalahan pada tingkat karakter mendapatkan akurasi 83,76%. Sedangkan
pada penelitian ini, kami menawarkan suatu inovasi berupa pengimplementasian algoritma
Recurrent Neural Network (RNN) untuk menormalisasikan kembali aksara arab tanpa
harakat menjadi tulisan arab yang lengkap dengan harakat. Model kami adalah model
pertama di dunia yang mencoba memulihkan kata-kata Bahasa Arab yang sepenuhnya
tanpa harakat dengan kalimat yang sepenuhnya berharokat menggunakan dataset training
berbasis al-Qur'an dengan pencapaian akurasi 98% komponen arsitektur model LSTM
yaitu 3 layer (64, 128, 64), optimizer “adam”, dan activation “relu”.
Kata kunci: Al-Qur’an, Pelengkap Harakat Arab, Long Short-Term Memory (LSTM)

Keywords

Al-Qur'an Pelengkap Harakat Arab Long Short-Term Memory (LSTM)

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How to Cite
Risa Riski Amalia, Asti Dwi Sripamuji, & Agi Prasetiadi. (2023). AUTOMATIC QURANIC ARABIC COMPLETION FOR QURAN LANGUAGE BASED ON RECURRENT NEURAL NETWORK. Khazanah: Jurnal Mahasiswa, 15(02). https://doi.org/10.20885/khazanah.vol15.iss2.art3
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