<![CDATA[Backpropagation is one of the artificial neural network algorithms widely used in classification and prediction processes due to its ability to recognize data patterns accurately. However, the performance of this algorithm is highly influenced by the quality of the input data. Unstructured data, differences in data scales, missing values, and irrelevant features can reduce the model’s accuracy. This study aims to analyze the effect of integrating data pre-processing strategies to optimize the accuracy of the Backpropagation algorithm. The dataset used in this research was obtained from the Badan Pusat Statistik (BPS) in the form of Open Unemployment Rate data for the population aged 15 years and above in North Sumatra Province from 2019 to 2024. The applied pre-processing stages included data cleaning, normalization, missing value handling, and feature reduction. The research method was conducted by comparing the model testing results using standard pre-processing and partial pre-processing on several network architectures. The results showed that the implementation of pre-processing strategies was able to improve the performance of the Backpropagation model. The highest accuracy value was obtained in the 3-56-1 architecture with an increase from 80.00% to 85.88%. In addition to improving accuracy, the model training process became more stable and the error convergence was achieved faster. Therefore, the integration of data pre-processing strategies has proven to be effective in optimizing the accuracy of the Backpropagation algorithm for numerical data-based prediction problems]]>
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