| Summary: | A flood is a natural disaster that cannot be stopped, but preventive measures can be taken to deal with it. The
factors that cause flooding can be predicted using machine learning, one of which is by predicting rainfall.
But in reality, rainfall data has many shortcomings, such as missing values and the appearance of outliers
that can affect model performance. Therefore, we propose an ensemble stacking method to deal with this
problem. The performance value of the Multilayer Perceptron algorithm without Stacking is 10.128 for MSE
and1.5696 for MAE. The performance value of the XGBoost algorithm without stacking is 9.2548 for MSE
and 1.4427 for MAE. While the performance value of combining the Multilayer Perceptron and XGBoost
algorithm with Stacking resulted in an MSE value of 9.2377 and an MAE value of 1.4396. The results show
that the ensemble method with stacking can be a solution to improve algorithm performance on weak datasets
to predict rainfall value. The novelty of this paper is as follows: machine learning ensembles can handle the
weak rainfall dataset to give a better result
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