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All Journal UNP Journal of Statistics and Data Science
Sherly Amora Jofipasi
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Social Sciences

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Prediction Of Bogor City Rainfall Parameters Using Long Short Term Memory (LSTM) Sherly Amora Jofipasi; Admi Salma; Dodi Vionanda; Dina Fitria
UNP Journal of Statistics and Data Science Vol. 1 No. 5 (2023): UNP Journal of Statistics and Data Science
Publisher : Departemen Statistika Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/ujsds/vol1-iss5/110

Abstract

Bogor is a city that has high intensity of rainfall and has erratic rainfall. So it is necessary to predict Bogor's rainfall. Rainfall prediction can be done using the LSTM algorithm. In the LSTM algorithm, there are neuron hidden layer and epoch parameters. Neuron hidden layer and epoch greatly affect the resulting prediction results, therefore it is necessery to determine the best neuron hidden layer and epoch values to produce good prediction results in Bogor rainfall. The prediction parameters results obtained by LSTM have worked well using optimal neuron hidden values of 256, optimal epoch of 150, MAPE of 1,64%, and the comparison of actual data patterns and prediction data already has the same data patterns.
Co-Authors Admi Salma Dina Fitria Dodi Vionanda