Content-based Filtering and Web Scraping in Website for Recommended Anime

Abstract

Aim: This research aim is to determine the level of user satisfaction using the Delone and Mclean models obtained from the implementation of the content-based filtering method in the anime recommendation system. Study Design: This study was designed with Delone and Mclean and with a Content-based Filtering method and web-scrapping to build an anime recommendation system. Place and Duration of Study: Department of Informatic Universitas Multimedia Nusantara, between July 2022 and December 2022. Methodology: The initial step in this research was collecting data using web scrapping and questionnaires, then followed by a literature study, and after that continued with system design and application development. After the application is made the next step is to get the level of user satisfaction with Delone and Mclean, and the final step is writing a report from this research. Results: The design and development of a system by implementing a content-based filtering method to the website-based have been successfully created, and the results of calculating the level of user satisfaction calculated from 43 respondents using the Delone and Mclean methods show, an anime recommendation system with content-based filtering methods has good result with a user satisfaction percentage of 74.23%. Conclusion: The anime system recommendation application has been successfully made and the results of user satisfaction are 74.23%.