Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Gaussian
Rahmatul Akbar
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Gaussian

 1 
PREDIKSI TINGKAT TEMPERATUR KOTA SEMARANG MENGGUNAKAN METODE LONG SHORT-TERM MEMORY (LSTM) Rahmatul Akbar; Rukun Santoso; Budi Warsito
Jurnal Gaussian Vol 11, No 4 (2022): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.11.4.572-579

Abstract

Temperature is one of the most important attributes of climate, temperature affects life in many different ways such as in agriculture, aviation, energy, and life in general. Temperature prediction is needed to make the right step to prevent the negative impact of climate change. Long Short-Term Memory (LSTM) is the method that can predict time series data, using the unique design of neural networks, LSTM can help to prevent vanishing gradient from happening which allows LSTM model to use more data from the past to predict the future. Hyperparameters like LSTM unit, epochs, and batch size are used to make the best model, the best model is the one with the lowest loss function. This research used climate data from 1 January 2019 until 31 December 2021 consist of 1096 data in total. The best prediction in this research is made by the model with 70% training data, 0,009 learning rate, 128 LSTM unit, 16 batch size, and 100 epochs with the lowest loss function of 0,013, this model gives MAPE value of 1,896016% and RMSE value of 0,725.
Co-Authors Budi Warsito Rukun Santoso