Rainfall Prediction in Jayapura City Area Using Long Short-Term Memory

Abstract

Jayapura, one of Indonesia’s major fishing cities, relies heavily on accurate weather predictions to ensure the safety of its fishermen, particularly due to its significant tuna and skipjack production. This study aims to improve rainfall forecasting in Jayapura using a Long Short-Term Memory (LSTM) model, a type of artificial neural network designed for time series prediction. Accurate rainfall forecasts are crucial for reducing the risks fishermen face at sea due to sudden weather changes. Daily data from the Meteorological Station in Dok II Jayapura was collected and processed to train the LSTM model, incorporating variables such as TAVG (average temperature), RH_AVG (average relative humidity), FF_AVG (average wind speed), Pressure (air pressure), and Wind_Gust (wind gust). The model’s performance was evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), yielding low values of 0.0542 and 0.0847, respectively, indicating high prediction accuracy. The MAE reflects the average magnitude of errors, while the RMSE highlights the model’s sensitivity to larger deviations, both supporting the reliability of the LSTM approach. The findings demonstrate that LSTM models can effectively forecast rainfall in Jayapura, providing valuable information that helps fishermen plan their activities more safely and efficiently. The study concludes that LSTM is a robust tool for rainfall prediction, and the inclusion of additional meteorological variables has proven to enhance accuracy. Further research is recommended to explore other factors to improve prediction reliability.