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All Journal Jurnal Informatika dan Teknologi Komputer ( J-ICOM) The Indonesian Journal of Computer Science
Trigreisian, Alwizain Almas
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Studi Literatur: Prediksi Kata Berikutnya dengan Metode Recurrent Neural Network Trigreisian, Alwizain Almas; Harani, Nisa Hanum
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 1 (2024): Jurnal Informatika dan Teknologi Komputer ( JICOM)
Publisher : Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55377/j-icom.v5i1.8104

Abstract

Next-word prediction is one of the most frequently used tasks in natural language processing. The Recurrent Neural Network (RNN) method is one method that has been proven to be effective in predicting the next word in a sentence, as it is capable of processing text data with order and context. In this research, various algorithms used in the development of next word prediction using the RNN method were analyzed. Some of these algorithms include LSTM (Long Short-Term Memory) and bidirectional LSTM. The results of this research show that the use of the RNN method in predicting the next word is able to provide better results compared to other methods. However, there are still some challenges that need to be overcome in developing the RNN model to predict the next word. Therefore, further research needs to be done in overcoming these challenges so that the use of the RNN method can be further optimized in predicting the next word in a sentence.
Next Word Prediction for Book Title Search Using Bi-LSTM Algorithm Trigreisian, Alwizain Almas; Harani, Nisa Hanum; Andarsyah, Roni
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3233

Abstract

Finding a suitable book title is still quite difficult at the moment. We often guess what book title we want, but in reality the book title is often not available. This research aims to overcome these problems by producing an accurate and efficient prediction model in predicting the next words in book title search using a deep learning algorithm, namely Bidirectional Long Short Term Memory (Bi-LSTM). The research stages consist of data collection, data preprocessing, data modeling, evaluation, and implementation. This research uses a dataset of Indonesian book titles obtained from the bukukita.com online bookstore website with 5618 data. The results show that the resulting deep learning model can predict the next words in the book title search with an accuracy of 81.82%. The model is implemented in the form of a web application using the Django framework, Python language, and MySQL database.
Co-Authors Andarsyah, Roni Harani, Nisa Hanum