<![CDATA[Weather prediction is one of the growing challenges in meteorological science. Accurate prediction methods can provide invaluable information for various sectors, including agriculture, transport, and natural disaster mitigation. One approach used in predicting weather is using artificial neural network (ANN) techniques. JST is a computational model inspired by the structure and function of human biological neural networks. This research aims to implement and evaluate the performance of JST in weather prediction. The data used is historical weather data that includes parameters such as air temperature, humidity, air pressure, and wind direction. The training process is carried out using a suitable learning algorithm to adjust the weights in the JST to produce accurate weather predictions. The results of this study show that a single hidden layer with only two nodes performs slightly better than more complex architectures. In addition, it requires a much shorter training time. In terms of accuracy and efficiency despite using a simpler architecture, the small network almost achieved the same accuracy (around 89%) as the original network. In addition, its training time is also more efficient. So based on these findings, it was decided to continue with the optimized network layout (one hidden layer with 2 nodes) due to the good balance between accuracy and efficiency. This research not only improves the accuracy of weather prediction but also highlights the importance of neural network architecture optimization according to the specific dataset and task.]]>
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