<![CDATA[Thyroid disease is a fairly common endocrine disorder that requires rapid and accurate diagnosis so that patients can receive appropriate treatment. This study was conducted to improve the system's ability to classify thyroid disease by utilizing data preprocessing techniques with RobustScaler and Random Over Sampling (ROS), as well as the Backpropagation Neural Network (BPNN) algorithm. The research dataset consisted of 3,771 patient data with 25 clinical attributes describing the condition and function of the thyroid. The data preprocessing process involved data selection, data cleaning, and data transformation using RobustScaler so that each feature had a more stable scale and was not affected by extreme values. The class imbalance problem was overcome using ROS so that the amount of data increased to 6,834 samples and the class distribution became more balanced. The Backpropagation Neural Network algorithm was applied in model training by testing various variations in the number of neurons in the hidden layer (38 and 49) and learning rate (0.01 and 0.001). Training was conducted for 5,000 and 10,000 epochs. Evaluation was performed using the 10-Fold Cross Validation method to obtain more consistent results. The results of the study show that the model is capable of achieving very high accuracy, up to 99.85%, on several parameters. The results show that proper data processing and appropriate parameter selection greatly affect model performance. Overall, the use of RobustScaler and ROS has been proven to significantly improve the accuracy of thyroid disease classification.]]>
