Utilisation of Naive Bayes Classifier in Sales Data Analysis Products to Improve Marketing Strategy (Case Study of Naive Bayes Classifier Algorithm Application at UD. Semut Ireng)

Utilisation of Naive Bayes Classifier in product sales data analysis at UD. Semut Ireng shows a model that has a classification accuracy of 79%. This research focuses on the application of data mining to support marketing strategies by predicting snack sales in June. The analysis method uses a data set of 208 that has gone through the initial stage of Knowledge Discovery in Database (KDD), consisting of 47 data with Restock Yes and 161 with Restock No. Restock No indicates that the sales analysis shows items that will be restocked, while Restock Yes indicates items that will be added. This research provides important insights to improve the marketing strategy of UD. Semut Ireng through data-based analysis.