<![CDATA[Diarrhea is the second disease that causes death in children in the world. Every year, around 1.7 million cases of diarrhea are found and cause around 525,000 deaths in children under the age of five in the world. Proper analysis of health service data can help predict epidemics, cure, and disease, and improve quality of life and avoid preventable deaths. This research is aimed at predicting diarrhea sufferers in the future by using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Seasonal Autoregressive Integrated Moving Average with explanatory X (SARIMAX) by involving climate factors in the form of average temperature and average humidity. The data used are data of diarrhea sufferers and climate in 2010-2019 in the city of Bandung. The result shows that there is not significant relation between temperature or humidity and the diarrhea cases. However, the SARIMA model had performed better than the SARIMAX model with the addition of climate factors to predict the diarrhea case in Bandung. The predictive accuracy of the SARIMA model obtained is 78.6%.]]>
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