<![CDATA[Acute Respiratory Infections (ARI) is a health problem that often affects children and adults. For adults, acute respiratory infections are mild or common, but in children under five, this disease is a threat that can cause death. One of the causes of death due to acute respiratory infections is the incorrect diagnosis. This study aims to determine the level of accuracy and optimal neural network architecture in detecting ARI using the backpropagation method. The backpropagation method is a pattern recognition technique that is able to provide decision results based on trained data. This research was implemented using MATLAB software with several forms of network architecture. Symptoms of ARI that were used as input for detection of the disease consisted of 13 variables targeting non-pneumonia and pneumonia ARDs. Based on the research results, the architecture with the best configuration consists of 13 input layer neurons, 20 hidden layer neurons and 2 output layer neurons with a binary sigmoid activation function (logsig), a learning rate value of 0.5, an error tolerance value of 0.001, a maximum of epoch of 216 and MSE 0.000997. Artificial neural networks with the backpropagation method used for weight adjustment can respond to training data and testing data well, marked by the resulting network accuracy 100% in accordance with the desired target.]]>
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