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All Journal JOIN (Jurnal Online Informatika) International Journal of Artificial Intelligence Research
Agung Bella Putra Utama
Department of Electrical Engineering, Universitas Negeri Malang
Computer Science & IT Electrical & Electronics Engineering

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Journal Unique Visitors Forecasting Based on Multivariate Attributes Using CNN Aderyan Reynaldi Fahrezza Dewandra; Aji Prasetya Wibawa; Utomo Pujianto; Agung Bella Putra Utama; Andrew Nafalski
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : International Journal of Artificial Intelligence Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (379.839 KB) | DOI: 10.29099/ijair.v6i1.274

Abstract

Forecasting 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.
PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis Agung Bella Putra Utama; Aji Prasetya Wibawa; Muladi Muladi; Andrew Nafalski
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.858

Abstract

Convolutional Neural Network (CNN) is an effective Deep Learning (DL) algorithm that solves various image identification problems. The use of CNN for time-series data analysis is emerging. CNN learns filters, representations of repeated patterns in the series, and uses them to forecast future values. The network performance may depend on hyperparameter settings. This study optimizes the CNN architecture based on hyperparameter tuning using Particle Swarm Optimization (PSO), PSO-CNN. The proposed method was evaluated using multivariate time-series data of electronic journal visitor datasets. The CNN equation in image and time-series problems is the input given to the model for processing numbers. The proposed method generated the lowest RMSE (1.386) with 178 neurons in the fully connected and 2 hidden layers. The experimental results show that the PSO-CNN generates an architecture with better performance than ordinary CNN.
Co-Authors Aderyan Reynaldi Fahrezza Dewandra Aji Prasetya Wibawa Andrew Nafalski Andrew Nafalski Muladi Utomo Pujianto