Machine Learning for Anime Recommendation System Using K-Means Clustering

Authors

  • Bonifasius Sean Pratama Department of Industrial Engineering and Management, Yuan Ze University, Taiwan
  • Elvin Nur Furqon Department of Mechanical Engineering, National Central University, Taiwan https://orcid.org/0000-0002-6529-6709
  • Christine Natalia Department of Industrial Engineering and Management, Yuan Ze University, Taiwan; Department of Industrial Engineering, Atma Jaya Catholic University of Indonesia, Indonesia https://orcid.org/0000-0002-4260-684X

DOI:

https://doi.org/10.24002/ijieem.v7i1.9402

Keywords:

artificial intelligence, anime, elbow methods, k-means clustering, machine learning, recommendation system

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.

Author Biography

Christine Natalia, Department of Industrial Engineering and Management, Yuan Ze University, Taiwan; Department of Industrial Engineering, Atma Jaya Catholic University of Indonesia, Indonesia

Christine Natalia is an Assistant Professor in the Department of Industrial Engineering, Faculty of Engineering, Atma Jaya Catholic University of Indonesia.  She has a Bachelor's Degree in Industrial Engineering from Institut Teknologi Sepuluh Nopember and received her Magister Teknik (M.T) Degree with Honour from Institut Teknologi Bandung. Her research interests lie in Operation Research, Logistics, and Transportation, especially in the Maritime sector. She published more than forty manuscripts, both in international and national journals. She also serves as a Reviewer and an Editor for some nationally accredited journals. Currently, she is pursuing her doctoral degree at Yuan Ze University, Taiwan.

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Zheng, L. (2019). Deep Learning on Recommender Systems. 1094340 Bytes.

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Published

2025-06-30

How to Cite

Bonifasius Sean Pratama, Elvin Nur Furqon, & 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. https://doi.org/10.24002/ijieem.v7i1.9402

Issue

Vol. 7 No. 1 (2025)

Section

Articles

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Copyright (c) 2025 Bonifasius Sean Pratama, Elvin Nur Furqon, Christine Natalia

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