Classifications of Offline Shopping Trends and Patterns with Machine Learning Algorithms

Abstract

Advancements in technology have made online shopping popular among many. However, the use of offline marketing models is still considered a profitable and important way of business development. This can be seen in the 2022 Association of Retail Entrepreneurs of Indonesia (APRINDO), which states that 60% of Indonesians shop offline, and in 2023, more than 75% of continental European consumers will prefer to shop offline. This is because many benefits can be achieved through offline marketing that cannot be obtained from online marketing. Therefore, classification of patterns and trends is performed to compare the results of the algorithms under study. Furthermore, this research was conducted to help offline retailers understand consumption patterns and trends that affect purchases. The algorithms analyzed in this study are K-Nearest Neighbor (K-NN), Naive Bayes, and Artificial Neural Network (ANN). As a result, the ANN algorithm obtained the highest confusion matrix results with an Accuracy value of 96.38%, Precision of 100.00%, and Recall of 100.00%. Meanwhile, when the Naive Bayes algorithm was used, the lowest Accuracy value was 57.39%, the Precision value was 57.86%, and when the K-NN algorithm was used, the Recall value was as low as 92.00%. These results indicate that the ANN algorithm is better at classifying offline shopping image data than the K-NN and Naive Bayes algorithms