Design and Development Of A Soccer Shoes Recommendation Application Using NLP Model Implementation and Content-Based Filtering

Abstrak

Public interest in sports is increasing, including football, which is also supported by the development of sports facilities. In football, shoes are an important piece of equipment that must meet high quality standards and provide comfort when worn. Comfortable shoes can influence a player's performance and reduce the risk of injury. To prevent risks resulting from choosing inappropriate shoes, a solution is needed by building a soccer shoe recommendation application. This research aims to develop a web-based application that uses Natural Language Processing (NLP) technology. This technology functions to understand user preferences based on the answers given in the question and answer system and the search feature. This recommendation system combines the Content-Based Filtering method with NLP. The Content-Based Filtering method recommends shoes based on characteristics that users have liked before, while NLP is used to analyze product descriptions, user reviews and their needs. The research results show that this application is able to provide relevant shoe recommendations with 87% accuracy. In addition, testing proves that the integration of Natural Language Processing and Content-Based Filtering makes the process of getting recommendations and choosing shoes more efficient and increases user satisfaction. This application is expected to help soccer players choose shoes that suit their needs.

Identifier

• OpenAlex: https://openalex.org/W7117586009
• DOI: https://doi.org/10.33795/jartel.v15i3.6965