<![CDATA[Indonesia's climate diversity leads to different rainfall patterns in each region. This condition presents a major challenge in the effort to produce accurate rainfall predictions, which are important to support effective infrastructure planning and disaster mitigation. The purpose of this research is to analyze the rainfall potential in Purwodadi Sub-district using Random Forest algorithm. In this analysis, several weather parameters such as air pressure, temperature, humidity, and wind speed are used, while rainfall becomes the target variable in the prediction process. The dataset used in this study was obtained from NASA Prediction Of Worldwide Energy Resources (POWER) with a time period between 2000 and 2022. The data is then divided into 70% for training data and 30% for test data. In this study, the Random Forest algorithm was used to classify the likelihood of rain based on existing weather conditions. The implementation results showed that the Random Forest model achieved 100% accuracy on the training data and 92% on the test data, indicating excellent prediction performance. Results from the confusion matrix confirmed that the majority of the model predictions matched the actual data. This finding shows that the weather parameters used are effective in predicting rainfall in Purwodadi sub-district. This research contributes to improving the accuracy of rainfall prediction and opens up opportunities for the development of better weather prediction models, involving more parameters or using other algorithms for more in-depth performance evaluation.]]>
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