ANALYSIS OF VIRAL WARUNK SALES DATA USING THE APRIORI ALGORITHM METHOD TO DETERMINE CUSTOMER PURCHASING PATTERNS
Abstract
This research aims to analyze Warunk Viral sales data using the Apriori algorithm method in order to determine consumer purchasing patterns. Warunk Viral is a small shop that sells various types of food and drink products. In this digital era, it is important for business owners like Warunk Viral to understand their consumer behavior in more depth in order to increase operational efficiency and profits.
The Apriori algorithm method is used in this research to identify products that are frequently purchased by consumers. By analyzing collected sales data, this research will reveal purchasing patterns that may not be immediately apparent, such as associations between certain products. The results of this analysis will provide valuable insight to Warunk Viral owners in managing stock, determining pricing strategies, and designing more effective promotions.
The research method used involves collecting Warunk Viral sales data over a certain period of time, processing the data, and applying the Apriori algorithm to generate relevant association rules. This data analysis can help Warunk Viral make smarter decisions in inventory management and improve the consumer shopping experience.
It is hoped that the results of this research can contribute to further understanding of consumer behavior at Warunk Viral and can also be applied in various other industries. In conclusion, this research proves that data analysis using the Apriori algorithm method can be an effective tool in understanding consumer purchasing patterns and helping companies to improve operational efficiency and increase their profits.
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