Implementasi Recurrent Neural Network dalam Memprediksi Kepadatan Restoran Berbasis LSTM

Authors

  • Annisa Farhah Telkom University, Bandung
  • Anggunmeka Luhur Prasasti Telkom University, Bandung
  • Marisa W Paryasto Telkom University, Bandung

DOI:

https://doi.org/10.30865/mib.v5i2.2916

Keywords:

Prediction, Recurrent Neural Network, Long Short Term Memory

Abstract

In this modern era, restaurants are becoming very popular, especially in big cities. However, this can lead to density or queues of visitors at a restaurant, which should be avoided during the current Covid-19 pandemic. So that accurate information that can predict the density of restaurant will be very useful. In predicting the density of restaurants, data processing on the number of visitors obtained from one of the restaurants is carried out using artificial intelligence in the form of LSTM (Long Short Term Memory) RNN (Recurrent Neural Network). The results of the research on Recurrent Neural Network based on LSTM (Long Short Term Memory) at the best learning rate parameter of 0.001 and a maximum epoch of 2000 resulted in an MSE value of 0.00000278 on the training data and 0.0069 on the test data

Author Biographies

Annisa Farhah, Telkom University, Bandung

Fakultas Teknik Elektro, Prodi S1 Teknik Komputer

Anggunmeka Luhur Prasasti, Telkom University, Bandung

Fakultas Teknik Elektro, Prodi S1 Teknik Komputer

Marisa W Paryasto, Telkom University, Bandung

Fakultas Teknik Elektro, Prodi S1 Teknik Komputer

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Published

2021-04-25

Issue

Vol. 5 No. 2 (2021): April 2021

Section

Articles

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