The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

Authors

  • Shofura Telkom University
  • Sri Suryani Prasetiyowati Telkom University
  • Linda Salma Jacobs University
  • Sri Harini UIN Maulana Malik Ibrahim

DOI:

https://doi.org/10.21108/ijoict.v7i2.602

Keywords:

backpropagation, artificial neural networks, predictions, monthly weather, rainfall

Abstract

Current weather-related research only focuses on weather prediction based on raw data and the factors used are generally 4 factors: average temperature, solar radiation, air pressure, and wind. In this research, monthly weather prediction is done using 5 factors where the additional factor used is rainfall in the previous time. In contrast to previous prediction research, the prediction process carried out in this study emphasizes the modeling of training data according to the desired prediction model.. These two things distinguish this research from previous studies. The prediction model used in this study is a classification-based prediction model that is the Artificial Neural Network (ANN) method combined with the backpropagation algorithm for calculating the weight of the ANN network. The data used are meteorological data from 2010 to 2018 in the Bogor area, where data from 2010 to 2016 are used as training data, and data from 2017 to 2018 are used as test data. The results of this study indicate that the design of the model with the use of data for 6 years with feature data of 5 factors has an accuracy rate of 83.33%.

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Published

2021-12-30

How to Cite

Shofura, S., Suryani M.Si, S., Salma, L., & Harini, S. (2021). The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks. International Journal on Information and Communication Technology (IJoICT), 7(2), 23–35. https://doi.org/10.21108/ijoict.v7i2.602

Issue

Vol. 7 No. 2 (2021): December 2021

Section

Theoretical Computer Science & Statistic

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