PENERAPAN ALGORITMA APRIORI UNTUK MENENTUKAN REKOMENDASI ANIME RATING TINGGI DENGAN JUMLAH PENONTON SEDIKIT (Studi Kasus: Situs MyAnimeList.net) - Diah Puspita, Dwi Ratna Ayu; Mashuri, Chamdan; Ahmad Heru Mujianto; Indana Lazulfa (Inovate, 2025)
Abstract High-quality yet less popular anime recommendations are a need for users on MyAnimeList.net, a platform containing thousands of anime titles with varying ratings and viewership numbers. This study aims to apply the Apriori algorithm to determine anime recommendations with high ratings but low viewership, using data from MyAnimeList.net. In the growing anime industry, many high-quality anime titles receive little attention from viewers. Therefore, it is essential to develop a recommendation system that can help users discover anime they may have missed. The dataset used consists of rating and viewership data from MyAnimeList.net. Through filtering and analysis, the Apriori algorithm successfully identified relationships among anime titles that meet the criteria of high ratings and low viewership, resulting in more targeted recommendations. This research is expected to contribute to the development of data mining-based recommendation systems, especially for recommending content that is rarely viewed yet has high quality. The use of the Apriori algorithm in content recommendations also shows potential in helping users find preferences that are more diverse and relevant to their interests.