Abstract
The development of information technology has driven digital transformation in the trade sector, one of which is through e-commerce platforms. In an effort to improve user experience and sales efficiency, this study developed an e-commerce application that is equipped with a machine learning-based buyer choice analysis feature. The app is designed not only as a buying and selling medium, but also capable of learning user preferences based on interaction data, such as search history, product clicks, and transactions. The machine learning methods used are classification and recommendation algorithms, especially K-Nearest Neighbors (KNN) and Collaborative Filtering, to predict the products that are most likely to be of interest to users. The development process follows the Agile Development model, with stages of UI/UX design, backend system integration, and model accuracy testing. The results of the evaluation showed that the recommended system implemented was able to increase the relevance of the product display by up to 85%, as well as provide a better personalized experience for users. This application is expected to be a smart solution in improving customer satisfaction and the effectiveness of digital marketing strategies
