<![CDATA[Weather forecasting are very important in various fields of human life, including in big cities. The need for accurate weather forecasts will be effective and efficient in managing the quality of civilization flexibly. In many cases it is found that the results of weather forecasts in the same city differ depending on the radius. This of course requires a precise and accurate algorithm to determine it. The algorithm used is based on machine learning type of artificial neural network which compares backpropagation and bayessian regularization. The results obtained show that bayessian regularization outperforms backpropagation with the smallest MSE and the highest accuracy and the shortest computation time to determine sunny, cloudy, light rain and heavy rain forecasts. The unbalanced distribution of data causes fluctuations in the MSE calculation and accuracy. The addition of training will improve system performance which is indicated by a significant increase in accuracy. Likewise, decreasing the MSE can increase the accuracy of the system to reach the point of convergence. This is an indicator that the performance of Bayessian regularization is the recommended algorithm for forecasting weather in cities and their surroundings, even between provinces or between countries.]]>
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