Abstract
Medicine sales are an important aspect in the pharmaceutical industry that requires effective analytical strategies to improve business performance and understand consumer patterns. This research aims to analyze medicine sales using Decision Tree method.The Decision Tree method is used to identify patterns and main factors that influence medicine sales. Decision trees will help in understanding the hierarchy of these factors and provide a clear view of the relationships between variables. These clusters can help in determining market segmentation and more specific sales strategies.The medicine sales data used in this research involves variables such as type of medicine, price, time of sale, and promotions carried out. The results of this analysis are expected to provide in-depth insight into sales trends, consumer preferences, and key factors that can increase the efficiency and effectiveness of medicine marketing strategies. By implementing this approach, it is hoped that pharmaceutical companies can make more informed decisions, minimize risks, and improve overall medicine sales performance. This research also contributes to the development of sales analysis methodology in the context of the pharmaceutical industry. The results of the Apply Model Decision Tree algorithm, obtained a true positive cash classification accuracy value of 75%, true positive Credit 66.67% and true positive Qris 100% class precision with an overall accuracy value of 80%. The level of accuracy between decision tree predictions and data testing is very high. This proves it that the Decision Tree Algorithm is suitable as a model for classification in this research
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Citations by Year
| Year | Count |
|---|---|
| 2024 | 2 |