Implementasi Data Mining dengan Algoritma Apriori dalam Menentukan Pola Pembelian Aksesoris Laptop

Abstract

Consumer purchasing patterns are an important factor in the business world, which influences marketing strategies, stock management and company profits. In the context of the laptop accessories business, a deep understanding of consumer purchasing patterns is very necessary to increase operational efficiency and customer satisfaction. Data mining, as a powerful data analysis method, has become an effective tool in uncovering these patterns. One of the data mining algorithms that is often used to analyze association patterns is the Apriori algorithm. This research applies the Apriori algorithm to identify and analyze purchasing patterns for laptop accessories from transaction data obtained from a retail store. By analyzing this data, we can identify items that are frequently purchased together and purchasing patterns that may not be immediately apparent to humans. The results of this analysis provide valuable insight into consumer preferences, helping retail stores to design more effective marketing strategies. The results of this research can also be used to manage stock more efficiently. By knowing deeper purchasing patterns, retail stores can predict stock needs more accurately, reduce the risk of excess inventory, and optimize operational expenses. Thus, this research can help increase company profits and satisfy customers by providing accessories that suit their preferences. In the increasingly developing information era, the use of data mining and algorithms such as Apriori is becoming increasingly important. This research is an example of how data analysis can be used in the real world to support smarter and more efficient decision making in the laptop accessories business. As a result, a better understanding of consumer behavior and purchasing patterns can provide a strong foundation for developing successful business strategies.