<![CDATA[Daily weather prediction is crucial in supporting decision-making in the agriculture, transportation, disaster mitigation, and community activities influenced by atmospheric conditions. The weather in Bima City is dynamic, requiring a predictive model capable of learning sequential meteorological data patterns. This study aims to build a daily average temperature prediction model using the Long Short-Term Memory (LSTM) algorithm based on 2025 meteorological data. The contribution of this study is to develop an LSTM-based daily average temperature prediction model using multivariate meteorological data from Bima City combined with pre-processing steps in the form of missing value handling using moving averages, MinMaxScaler normalization, and time series data formation using a 30-day sliding window. This study also provides an initial evaluation of the application of LSTM to local meteorological data from Bima City, which has been studied only limitedly, as a basis for developing a deep learning-based weather prediction system. The variables used include minimum temperature (TN), maximum temperature (TX), average temperature (TAVG), average air humidity (RH_AVG), rainfall (RR), sunshine duration (SS), and average wind speed (FF_AVG). The test results show that the model produces a Root Mean Square Error (RMSE) value of 0.7242 and is able to follow the daily temperature change pattern in the actual data. The prediction results on the test data also show that most of the predicted values have a relatively small difference compared to the actual values, so the model is able to describe the daily temperature change pattern quite well. Based on the predicted weather parameters, the model is able to provide information about daily weather conditions, namely sunny, cloudy, and rainy, according to the values of rainfall, air humidity, and sunshine duration produced. This predicted information is expected to help the community as an initial picture of future weather conditions so that it can support the planning of various daily activities. However, the results of this study are still limited to one prediction method and have not been compared with other methods. Therefore, further research can conduct comparisons with other algorithms to improve the accuracy of weather predictions in Bima City.]]>
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