PREDIKSI CURAH HUJAN DI KABUPATEN SIDOARJO MENGGUNAKAN METODE GA-LSTM DAN MITIGASI BENCANA BANJIR MENGGUNAKAN RISK MATRIX FLOOD ESTIMATION

Linggar, Dienna Eries (2025) PREDIKSI CURAH HUJAN DI KABUPATEN SIDOARJO MENGGUNAKAN METODE GA-LSTM DAN MITIGASI BENCANA BANJIR MENGGUNAKAN RISK MATRIX FLOOD ESTIMATION. Undergraduate thesis, UPN VETERAN JATIM.

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Abstract

Climate change has become increasingly intense, leading to a rise in the frequency of extreme weather events, particularly heavy rainfall that can trigger flooding. Sidoarjo Regency, as a flood-prone area in East Java, requires a reliable daily rainfall prediction model to support risk-based disaster mitigation. This study develops and evaluates a daily rainfall prediction model using Long Short-Term Memory (LSTM) optimized with the Genetic Algorithm (GA), forming a hybrid GA LSTM model. GA is employed to determine the optimal combination of LSTM hyperparameters, such as the number of neurons, learning rate, batch size, and epochs, in order to enhance model performance. Daily rainfall data from 2020 to 2024 are used as the main dataset, with preprocessing stages including normalization and sequential data formation. The model is evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), along with k-fold cross-validation to ensure the stability and reliability of the predictions. The results show that the GA-LSTM model outperforms the standard LSTM, particularly in reducing squared error (MSE), with improved RMSE and MAE values. The predicted daily rainfall is then integrated with a Risk Matrix Flood Estimation to map flood risk levels both spatially and temporally in Sidoarjo Regency. This approach provides a robust foundation for more proactive, data-driven disaster mitigation decision-making. The study concludes that integrating GA-LSTM with a Risk Matrix can improve prediction accuracy and enhance flood mitigation effectiveness in tropical regions with high climate variability.

Item Type:

Thesis (Undergraduate)

Contributors:

Contribution	Contributors	NIDN/NIDK	Email
Author	Mohammad Idhom, Mohammad Idhom	NIDK19830310 202121 1 006	idhom@upnjatim.ac.id
Author	Trimono, Trimono	NIDK19950908 202203 1 003	trimono.stat@upnjatim.ac.id

Subjects:

T Technology > T Technology (General)
T Technology > T Technology (General) > T385 Computer Graphics

Divisions:

Faculty of Computer Science

Depositing User:

Dienna Linggar Eries

Date Deposited:

28 Jul 2025 08:02

Last Modified:

28 Jul 2025 08:02

URI:

https://repository.upnjatim.ac.id/id/eprint/41311