Prediksi Curah Hujan Bulanan Di Medan Menggunakan Metode Long Short-Term Memory (LSTM)

Abstract

This study aims to predict monthly rainfall in Medan City using the Long Short-Term Memory (LSTM) method. The data utilized in this research comprises monthly rainfall figures and the number of rainy days for the 2015–2023 period, obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) Region I Medan via official publications of the Central Statistics Agency (BPS) of North Sumatra Province. The pre-processing stage involves data cleaning, normalization, and the construction of a time series dataset using a sliding window structure. The LSTM model was developed with two hidden layers and optimized using the Adam algorithm. Evaluation results indicate that the LSTM model effectively captures seasonal patterns and rainfall trends, as evidenced by a low Root Mean Square Error (RMSE) value. This study is expected to serve as a reference for hydrometeorological disaster mitigation in the Medan region.