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All Journal International Journal of Informatics Engineering and Computing
Hidayatulloh, M. Riyan
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Computer Science & IT

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Weather Forecasting in Denpasar City Using Stacked Long Short-Term Memory Algorithm (LSTM) Hidayatulloh, M. Riyan; Diqi, Mohammad; Wijaya Sugiarto, R. Nurhadi
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing [Preview]
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/408j8q02

Abstract

Forecasting the weather is essential to sustaining everyday human activities, particularly in industries like tourism, agriculture, and transportation. The effects of extreme weather events can be lessened by timely and accurate weather forecasts. This research suggests a Denpasar City weather forecasts are made utilizing a deep learning technique and the Stacked Long Short-Term Memory architecture. The four main parameters of the model— temperature, humidity, wind speed, and pressure—were trained using historical weather data spanning 1990 to 2020. A sliding window method was used to organize the dataset into time-series sequences after it had been preprocessed using normalization techniques. The Adam optimizer was used to train the model over 50 epochs with a batch size of 64. Four regression measures were used for evaluation: Coefficient of Determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The Stacked LSTM model's average MAE of 1.08, MAPE of 10.22%, RMSE of 1.93, and R2 of 0.86 demonstrate how well it captures temporal patterns and generates precise forecasts, according to the experimental data. These results show how the Stacked LSTM approach can be used to support decision-making in weather-sensitive domains and create automated weather forecasting systems.
Co-Authors Mohammad Diqi Wijaya Sugiarto, R. Nurhadi