<![CDATA[The advancement of information technology has encouraged companies to manage data more effectively in decision-making processes, including inventory management. Problems such as overstock, deadstock, and stockout frequently occur due to the lack of data-driven management based on historical transactions. This study aims to analyze and classify inventory demand levels using the Naïve Bayes algorithm to optimize inventory management at PT Soala Gogo Natama. This research employs a quantitative descriptive method with a case study approach. The dataset consists of 300 observations divided into training data (210) and testing data (90) with 9 variables. The analysis process includes data preprocessing, implementation of the Gaussian Naïve Bayes algorithm using Python, and model evaluation using accuracy, confusion matrix, and classification report. The results indicate that the model successfully classifies data into three categories: low, medium, and high, with an accuracy of 78.89%. Model evaluation shows good performance in the low and high classes, while misclassification occurs in the medium class due to overlapping characteristics between classes. This suggests that the model is effective in identifying data patterns but has limitations when handling data with similar distributions. In conclusion, the Naïve Bayes algorithm is effective in supporting inventory management decision-making, particularly in determining stock priorities based on historical data.]]>
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