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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA
Ayu Zulfiani
IIB Darmajaya, Lampung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Penerapan Algorimta Backpropagation Untuk Prakiraan Cuaca Harian Dibandingkan Dengan Support Vector Machine dan Logistic Regression Ayu Zulfiani; Chairani Fauzi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6173

Abstract

To anticipate the impacts caused by extreme weather, the Meteorology, Climatology, and Geophysics Agency (BMKG) issues weather forecasts so that the community can be prepared when such extreme weather occurs. The application of Artificial Neural Network (ANN) techniques in weather forecasting significantly enhances the ability to explore vast amounts of big data in obtaining the necessary information, serving as a reliable assistant for forecasting and policymaking. The data used in this study consists of weather elements such as pressure, air temperature, humidity, wind direction and speed, as well as rainfall, obtained from the Radin Inten II Lampung Meteorological Station. The observational data has a data density per hour, spanning a period of 5 years from January 1, 2018, to December 31, 2022. The method employed in this research is Backpropagation Neural Network (BPNN). The research results indicate that BPNN can effectively predict classified rainfall compared to other methods, within recall value when slight rain 0.68, moderate rain 0.17, and heavy rain 0.03, meanwhile Support Vector Machine (SVM) and Logistic Regression (LR) method can predict only slight rain with recall value when slight rain is 0.51 and 0.47.
Co-Authors Chairani Chairani