<![CDATA[The availability of essential medicines is a fundamental factor in ensuring high-quality healthcare services, especially in primary healthcare facilities such as Puskesmas. Inefficient drug inventory management can lead to various issues, including drug shortages that disrupt medical services and overstocking that may result in waste due to expiration. An accurate prediction system is essential to support more effective and efficient drug inventory planning. This study aims to analyze historical drug usage patterns to generate more accurate predictions. The research methodology includes problem identification, data collection, preprocessing, ANN architecture design, implementation, and system evaluation. Historical drug usage data from previous years is used for training and testing, with a division of 70% for training and 30% for testing. The backpropagation algorithm is applied to optimize the model by adjusting parameters such as the number of neurons in the hidden layer, learning rate, and activation function. The study results show that the ANN model with a 12-12-1 architecture achieves a high prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of 2.13% for paracetamol stock. The developed MATLAB application provides an interactive platform for users to input historical data and obtain dynamic stock predictions. This system implementation is expected to help Puskesmas manage drug inventory more effectively, reduce the risks of shortages and overstocking, and improve efficiency in essential drug distribution. This study contributes to the field of health informatics by demonstrating the effectiveness of ANN in drug inventory prediction. Future research may explore hybrid machine learning models or integrate external factors, such as seasonal disease patterns and community demand levels, to enhance predictive accuracy and adaptability.]]>
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