APPLICATION OF DATA MINING ALGORITHMS IN PATTERN ANALYSIS TO SUPPORT DECISION-MAKING
Keywords:
Data Mining, Decision Tree, C4.5 Algorithm, Pattern Analysis, Decision-Making.Abstract
The rapid growth of data across various sectors requires effective techniques to extract meaningful information that can support decision-making processes in organizations. This study aims to apply data mining algorithms to analyze patterns within datasets and generate useful insights for decision-making activities. The research uses a quantitative approach with a classification method, specifically the Decision Tree (C4.5) algorithm, to process and analyze sales data from the food and beverage sector. The research stages include data collection, data preprocessing, model construction, and evaluation using a confusion matrix to measure performance. The results show that the developed model can identify important patterns in the dataset, achieving an accuracy rate of 88%, which indicates good classification performance. These findings demonstrate that data mining techniques are effective in supporting decision-makers to understand trends, predict future outcomes, and improve strategic planning. Therefore, the application of data mining algorithms provides a reliable and practical solution for transforming raw data into meaningful information that can enhance decision-making quality in various organizational contexts and support data-driven business strategies effectively
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