A Product Recommendation-Based E-Commerce Application Using Collaborative Filtering at Izra Fashion Store

Abstract

Digital transformation in the world of trade has shifted to E-Commerce in order to increase market share and efficiency. Izra Fashion Store, which is engaged in selling hijab, currently still relies on social media for marketing and interaction with customers. Although this method is quite effective, a more structured solution is needed so that the sales process can run more optimally. The use of a marketplace platform such as Shopee is actually an option, but tight competition, the number of similar stores, and the difficulty of getting positive reviews in a short time make it difficult for small stores to build customer trust. Therefore, this study aims to build a website-based e-commerce equipped with a product recommendation feature using the Collaborative Filtering method. Researching it using quantitative methods with the help of a descriptive approach. Data was collected through data collection and analysis of user needs, which were then used to implement the Collaborative Filtering algorithm in the product recommendation system. This system is assisted by the PHP programming language and MySQL database. The research findings show that the system built is able to improve the customer shopping experience by providing product recommendations that are more in line with their preferences. In addition, this system also helps business owners manage product and transaction data in a more organized manner. Through the existence of this e-commerce platform.