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All Journal International Journal of Industrial Engineering and Engineering Management
Bonifasius Sean Pratama
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Industrial & Manufacturing Engineering

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Machine Learning for Anime Recommendation System Using K-Means Clustering Bonifasius Sean Pratama; Elvin Nur Furqon; Natalia, Christine
International Journal of Industrial Engineering and Engineering Management Vol. 7 No. 1 (2025)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v7i1.9402

Abstract

The 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.
Machine Learning for Anime Recommendation System Using K-Means Clustering Bonifasius Sean Pratama; Elvin Nur Furqon; Natalia, Christine
International Journal of Industrial Engineering and Engineering Management Vol. 7 No. 1 (2025)
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijieem.v7i1.9402

Abstract

The 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.
Co-Authors Christine Natalia Elvin Nur Furqon