[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122410-en":3,"doc-seo-122410-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122410,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Machine Learning for Anime Recommendation System Using K-Means Clustering","Rising global interest in Japanese-origin “anime” drives demand for personalized viewing recommendations, since many fans and casual viewers need guidance to find series matching their preferences. This research proposes a recommender system that suggests related anime titles for the series entered by the user. K-Means clustering is employed to build clustering models from data series, while the Elbow Method selects an appropriate number of clusters. The results indicate iterative recommendation outputs tied to the initial input.","Machine Learning for Anime Recommendation System Using K-Means Clustering  \nBonifasius Sean Pratama1, Elvin Nur Furqon2, Christine Natalia1,3  \n1Department of Industrial Engineering and Management, Yuan Ze University, Taiwan 2Department of Mechanical Engineering, National Central University, Taiwan 3Department of Industrial Engineering, Atma Jaya Catholic University of Indonesia, Indonesia  \nEmail: [s1115447@mail.yzu.edu.tw](s1115447@mail.yzu.edu.tw), [s1114824@mail.yzu.edu.tw](s1114824@mail.yzu.edu.tw), [chrisnatalia@atmajaya.ac.id](chrisnatalia@atmajaya.ac.id)  \n*Corresponding author  \nABSTRACT  \nThe increasing popularity of Japanese-origin animation industries or so-called “anime” attracts more interest from already-known fans and ordinary people who are just interested in watching. However, many viewers need advice in the form of recommendations for their preferred anime. This research aims to help viewers by developing a system that could provide some recommendations for several anime series related to the current series watched by the viewers. On the other side, this research could provide a reference to other researchers, especially those whose research focuses on Machine Learning, Artificial Intelligence, and Japanese Animation culture. In this paper, the K-Means Clustering method is used to build the clustering model based on the data series, and the Elbow Method is used to determine the appropriate number of clusters. The result of this research indicates that the system can provide several titles of anime series related to the initial title of the anime series entered by the user at each iteration.  \nDOI: [https://doi.org/10.24002/ijieem.v7i1.9402](https://doi.org/10.24002/ijieem.v7i1.9402)  \nKeywords: artificial intelligence, anime, elbow methods, k-means clustering, machine learning, recommendation system  \nResearch Type: Research Paper  \nArticle History: Received June 18, 2024; Revised October 19, 2024; Accepted November 2, 2024  \nHow to cite: Pratama, B.S., Furqon, E.N., & Natalia, C. (2025) . Machine learning for anime recommendation system using k-means clustering. International Journal of Industrial Engineering and Engineering Management, 7(1), 33-42.  \n© 2025 The Author(s) . This work published in the International Journal of Industrial Engineering and Engineering Management, which is an open access article under the CC BY 4.0 license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \n1. INTRODUCTION  \nAnime is a branch of animation originating from Japan. The global popularity and accessibility of anime through streaming platforms, conventions, merchandise, and fan communities have contributed to the growth of anime fandom worldwide. Viewers from different countries and cultural backgrounds have embraced anime as a form of entertainment, art, and cultural exchange, leading to a diverse and vibrant anime community that transcends geographical boundaries. The ability to connect with fellow fans, participate in discussions, attend  \nevents, and engage with anime content online has enriched the anime-watching experience for viewers around the world, allowing them to share their love for anime and discover new series and genres. With its broad range of themes and genres that attract audiences from various age groups, anime has gained considerable popularity in recent decades. According to the World Population Review website (Anime Popularity by Country 2024, n.d.), interest in anime in 2024, based on the Google search term, is very high among people in China, followed by Egypt and Yemen. The same website reports that Japan has the highest percentage of its  \npopulation watching anime (75.87%), followed by the United States (71.86%) . The development of streaming services at the beginning of the 21st century made anime more accessible and enjoyable for audiences worldwide.  \nAnime preferences are shaped by a multitude offactors, including genre, art style, narrative ","cbCaicokal7NugnP","https://ap.wps.com/l/cbCaicokal7NugnP","pdf",654476,1,10,"English","en",105,"# Introduction\n## Background on anime popularity and viewer interests\n## Need for recommendation systems\n## Relevance of machine learning and deep learning\n# Method Overview\n## K-Means clustering model building\n## Elbow Method for determining cluster count","[{\"question\":\"What is the main goal of the proposed anime recommendation system?\",\"answer\":\"To recommend related anime series based on the initial title entered by the user.\"},{\"question\":\"Which clustering technique is used to build the model?\",\"answer\":\"K-Means Clustering is used to construct the clustering model from the data series.\"},{\"question\":\"How is the number of clusters determined in the research?\",\"answer\":\"The Elbow Method is applied to select an appropriate number of clusters.\"}]","Machine Learning for Anime Recommendation System Using K-Means Clustering | PDF",1785810488,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-anime-recommendation-system-using-k-means-clustering","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-anime-recommendation-system-using-k-means-clustering/122410/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the proposed anime recommendation system?","Question",{"text":75,"@type":76},"To recommend related anime series based on the initial title entered by the user.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which clustering technique is used to build the model?",{"text":80,"@type":76},"K-Means Clustering is used to construct the clustering model from the data series.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the number of clusters determined in the research?",{"text":84,"@type":76},"The Elbow Method is applied to select an appropriate number of clusters.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]