Prediksi Kunjungan Wisatawan dengan Reduksi Noise pada Google Trends menggunakan Hilbert-Huang Transform dan Long Short-Term Memory

  • Harun Mukhtar Universitas Muhammadiyah Riau
  • Yoze Rizki Universitas Muhammadiyah Riau
  • Febby Apri Wenando Universitas Andalas
  • Muhammad Abdul Al Aziz
Keywords: Tourism, Noise, Hilbert Huang Transform, Long Short-Term Memory, Mean Absolute Percentage Error, Root Mean Square Error

Abstract

 

In many studies, Google Trends Data is efficient to analyze and estimate as explanatory variables, including tourism predictions. However, data retrieval and tourism are always plagued by noise. Without noise processing, the predictive ability of search engine data may be weak, even invalid. As a noise processing method, Hilbert-Huang Transform (HHT) can reduce or clean noise. Forecasting is the art and science of predicting future events. LSTM is able to overcome long-term dependence. This study tries to provide predictions of tourist visits by processing noise in search engines using the Hilbert-Huang Transform method. The forecasting architecture that is built is composed of 3 hidden LSTM layers with 100 units of neurons or nerves that function to process information, which in the LSTM layer also becomes the input layer. Prediction test results on a dataset of 156 rows, resulting in RMSE values in 2019 getting RMSE LSTM 129249 results, and RMSE HHT + LSTM 653058. so that the resulting RMSE is closer to remembering 0.

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Published
2022-12-30
Abstract views: 50 , PDF downloads: 30