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(Dept Electrical Engineering, State University of Malang, Malang)(2) * Aji Prasetya Wibawa
(Scopus ID : 56012410400; Dept Electrical Engineering, State University of Malang, Malang, Indonesia)(3) Utomo Pujianto
(Dept Electrical Engineering, State University of Malang, Malang)(4) Agung Bella Putra Utama
(Department of Electrical Engineering, Universitas Negeri Malang, Indonesia)(5) Andrew Nafalski
(University of South Australia, Australia)*corresponding author
AbstractForecasting is needed in various problems, one of which is forecasting electronic journals unique visitors. Although forecasting cannot produce very accurate predictions, using the proper method can reduce forecasting errors. In this research, forecasting is done using the Deep Learning method, which is often used to process two-dimensional data, namely convolutional neural network (CNN). One-dimensional CNN comes with 1D feature extraction suitable for forecasting 1D time-series problems. This study aims to determine the best architecture and increase the number of hidden layers and neurons on CNN forecasting results. In various architectural scenarios, CNN performance was measured using the root mean squared error (RMSE). Based on the study results, the best results were obtained with an RMSE value of 2.314 using an architecture of 2 hidden layers and 64 neurons in Model 1. Meanwhile, the significant effect of increasing the number of hidden layers on the RMSE value was only found in Model 1 using 64 or 256 neurons. |
DOIhttps://doi.org/10.29099/ijair.v6i1.274 |
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