Website Visitors Forecasting using Recurrent Neural Network Method
DOI:
https://doi.org/10.25126/jitecs.202162296Abstract
Abstract. The number of visitors and content accessed by users on a site shows the performance of the site. Therefore, forecasting needs to be done to find out how many users a website will come. This study applies the Long Short Term Memory method which is a development of the Recurrent Neural Network method. Long Short Term Memory has the advantage that there is an architecture of remembering and forgetting the output to be processed back into the input. In addition, the ability of another Long Short Term Memory is to be able to maintain errors that occur when doing backpropagation so that it does not allow errors to increase. The comparison method used in this study is Backpropagation. Neural Network method that is often used in various fields. The testing using new visitor data and first time visitors from 2018 to 2019 with vulnerable time per month. The computational experiment prove that the Long Short Term Memory produces better result in term of the mean square error (MSE) comparable to those achieved by Backpropagation Neural Network method.
References
H. Abbasimehr, M. Shabani, and M. Yousefi, "An optimized model using LSTM
network for demand forecasting," Computers & Industrial Engineering, vol. 143, p.
, 2020/05/01/ 2020.
A. Alfiyatin, A. M. Rizki, W. Mahmudy, and C. Fajri, "Extreme Learning Machine and
Particle Swarm Optimization for Inflation Forecasting," International Journal of
Advanced Computer Science and Applications, vol. 10, 2019.
Z. Berradi and M. Lazaar, "Integration of Principal Component Analysis and Recurrent
Neural Network to Forecast the Stock Price of Casablanca Stock Exchange," Procedia
Computer Science, vol. 148, pp. 55-61, 2019/01/01/ 2019.
U. Gunter and I. Onder, "Forecasting city arrivals with Google Analytics," Annals of
Tourism Research, vol. 61, pp. 199-212, 2016.
Haviluddin and N. Dengen, "Comparison of SARIMA, NARX and BPNN models in
forecasting time series data of network traffic," in 2016 2nd International Conference
on Science in Information Technology (ICSITech), 2016, pp. 264-269.
H. Haviluddin and R. Alfred, A genetic-based backpropagation neural network for
forecasting in time-series data, 2015.
Y. Huang, S. Liu, and L. Yang, "Wind speed forecasting method using EEMD and the
combination forecasting method based on GPR and LSTM," Sustainability, vol. 10, p.
, 2018.
X. Li, R. Law, G. Xie, and S. Wang, "Review of tourism forecasting research with
internet data," Tourism Management, vol. 83, p. 104245, 2021/04/01/ 2021.
V. Meilia, B. D. Setiawan, and N. Santoso, "Extreme Learning Machine Weights
Optimization Using Genetic Algorithm In Electrical Load Forecasting," Journal of
Information Technology and Computer Science, vol. 3, p. 77, 2018.
D. C. Montgomery, C. L. Jennings, and M. Kulahci, Introduction to Time Series
Analysis and Forecasting: Wiley, 2011.
A. Nurdiani, Pedoman Akreditasi Jurnal Ilmiah: Direktorat Jenderal Penguatan Riset
dan Pengembangan Kementerian Riset, Teknologi, dan Pendidikan Tinggi, 2018.
I. Oktanisa, W. Mahmudy, and G. Maski, "Inflation Rate Prediction in Indonesia using
Optimized Support Vector Regression Model," Journal of Information Technology and
Computer Science, vol. 5, p. 104, 2020.
H. Salehinejad, S. Sankar, J. Barfett, E. Colak, and S. Valaee, "Recent Advances in
Recurrent Neural Networks," 2017.
N. Sari, W. Mahmudy, and A. Wibawa, "Backpropagation on neural network method
for inflation rate forecasting in Indonesia," vol. 8, pp. 69-87, 2016.
S. Syahrial, M. Khairul, and M. Nunung, "Analisa Statistik Pengunjung Situs Resmi
Universitas Syiah Kuala (Www.unsyiah.ac.id)," Jurnal Rekayasa Elektrika, vol. 9, pp.
-54, 2010 2010.
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