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This is a Quantitative Study study.

September 16, 2025JTAM (Jurnal Teori dan Aplikasi Matematika)Open Access

Enhancing Weather Forecasting in Bandar Lampung: A Hybrid SARIMA-LSTM Approach

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The SARIMA-LSTM hybrid model achieved superior performance with MSE, RMSE, and MAPE values of 0.1174, 0.3426, and 0.0104%, respectively.
Using an 80%-20% training-testing split improved predictive outcomes, effectively capturing seasonal weather patterns in Bandar Lampung.
Hyperparameter optimization through grid search significantly enhanced the model's performance in forecasting complex weather.
Results can support public health strategies against diseases like malaria and dengue hemorrhagic fever, aiding disaster preparedness.

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Implication

This analysis utilizes a hybrid SARIMA-LSTM model, capturing seasonal variations and nonlinear relationships to enhance predictive accuracy for weather forecasting.

Cite This Study

Kurniasari et al. (2025) studied this question.

synapsesocial.com/papers/68d453a431b076d99fa5994c https://doi.org/10.31764/jtam.v9i1.27188

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Implication

This analysis utilizes a hybrid SARIMA-LSTM model, capturing seasonal variations and nonlinear relationships to enhance predictive accuracy for weather forecasting.

Enhancing Weather Forecasting in Bandar Lampung: A Hybrid SARIMA-LSTM Approach

Key Points

Discussion

Implication

Cite This Study

Enhancing Weather Forecasting in Bandar Lampung: A Hybrid SARIMA-LSTM Approach

Key Points

Discussion

Implication

Cite This Study

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