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Abstract
Bogor Regency is an area that often experiences prolonged rainfall, especially during the rainy season. High rainfall causes problems such as floods and landslides. Therefore, accurate rainfall prediction is important for various needs, especially in disaster mitigation. This study aims to implement the Long Short-Term Memory (LSTM) algorithm as a model for prediction of historical rainfall data and use the Large Language Model (LLM) GEMMA 2 to provide interpretation of prediction results and recommendations based on the prediction results. The methods used include data collection from the BMKG online data website totaling 1804 data, data pre-processing, model building, model performance evaluation, and interpretation of results using LLM. The results of this study show that LSTM is able to produce the best performance by showing MSE 201.92 mm², Root Mean Square Error (RMSE) of 14.21 mm. the RMSE value shows an average error of 14.21 mm. In addition, the interpretation provided by LLM GEEMA 2 to help understand the prediction and provide practical recommendations for disaster mitigation due to rainfall.
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