Open AccessWebsite Visitors Forecasting using Recurrent Neural Network Method
Author(s) -
Putu Bagus Arya,
Wayan Firdaus Mahmudy,
Achmad Basuki
Publication year - 2021
Publication title -
jitecs (journal of information technology and computer science)
Language(s) - English
Resource type - Journals
eISSN - 2540-9824
pISSN - 2540-9433
DOI - 10.25126/jitecs.202162296
Subject(s) - backpropagation , forgetting , artificial neural network , computer science , visitor pattern , term (time) , recurrent neural network , mean squared error , artificial intelligence , machine learning , rprop , long short term memory , data mining , types of artificial neural networks , statistics , mathematics , cognitive psychology , psychology , physics , quantum mechanics , programming language
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.