Analisa Data Shopping Trends Menggunakan Algoritma Klasifikasi Dengan Metode Naive Bayes - Andi Diah Kuswanto; Said Imam Puro; Jodi Hariyan; Ridho Rafliansyah; Muhammad Rival Aziz; Pebro Vaulina Rajagukguk (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2024)
In the era of rapid digitalization, understanding consumer behavior through data is becoming increasingly important for retail businesses. Shopping trends, such as those contained in this study, provide in-depth insights into various aspects of consumer behavior, from demographics to purchasing preferences and patterns of discount usage. This data is invaluable in formulating effective marketing strategies, improving customer experience, and optimizing business operations. The data used in this study included a variety of relevant variables, such as age, gender, location, product categories purchased, number of purchases, payment methods, and frequency of purchases. This information allows for a comprehensive analysis of how these factors affect consumer spending decisions. For example, analytics can reveal seasonal trends in purchases, product color and size preferences, and the impact of discounts and promo codes on sales volume. In addition, this dataset also reflects the changes in consumer behavior that have occurred over the past few years. Quantitative methodology is a research approach used to collect and analyze numerical data to understand patterns, relationships, and events in a given population. Data is collected from various sources such as online sales transactions, consumer surveys, Naive Bayesian algorithms are applied to the dataset that has been processed. The data was divided into two sets: training (80%) and testing (20%).