Analisis Preferensi Penonton Anime berbasiskan Genre Film menggunakan Metode K-Means

Niken Zalzabila(1*), Rastri Prathivi(2),

(1) Universitas Semarang, Indonesia
(2) Universitas Semarang, Indonesia
(*) Corresponding Author

Abstract


This study aims to analyze anime audience preferences based on genres using the K-Means clustering algorithm. The dataset consists of 100 popular anime titles with features such as ratings, votes, and genres. The research steps include data preprocessing, clustering with the Elbow method to determine the optimal number of clusters, and applying the K-Means algorithm. The clustering results revealed four clusters with unique characteristics, highlighting differences in popularity and genre preferences. Evaluation using the Confusion Matrix shows a model accuracy of 95%, while the Silhouette score of 0.285 indicates adequate cluster separation. These findings are expected to provide insights for streaming platforms to deliver more personalized and relevant anime recommendations to viewers.

Full Text:

PDF

References


G. P. Brahmantha, E. Utami, And A. Yaqin, “Klasifikasi Genre Anime Berdasarkan Sinopsis Menggunakan Algoritma K-Nearest Neighbors,” Jurnal Manajemen Informatika & Sistem Informasi (Misi), Vol. 7, No. 1, Pp. 15–24, 2024, Doi: 10.36595/Misi.V5i2.

Grand View Research, “Anime Market Size, Share & Trends Analysis Report By Type (T.V., Movie, Video, Internet Distribution, Merchandising, Music), By Region, And Segment Forecasts, 2021 - 2028.” Accessed: Oct. 02, 2024. [Online]. Available: Https://Www.Grandviewresearch.Com/Industry-Analysis/Anime-Market

Z. Muttaqin, C. Savitri, S. Suroso, And K. M. Gu, “Strategi Kolaborasi Uniqlo Dengan Program Anime Terhadap Hasil Penjualan: Studi Pada Uniqlo Dengan Program Anime,” Akademik: Jurnal Mahasiswa Humanis, Vol. 4, No. 3, Pp. 1056–1069, 2024, Doi: Https://Doi.Org/10.37481/Jmh.V4i3.995.

A. D. Pratama And A. Puspitasari, “Diplomasi Budaya Anime Sebagai Upaya Penguatan Soft Power Jepang Periode 2014-2018,” Balcony, Vol. 4, No. 1, Pp. 11–23, 2020.

M. Sakuma, “The Future Of Fansubs: Facing The Advent Of Legal Anime On Streaming Platforms,” Skase Journal Of Translation And Interpretation, Vol. 16, No. 1, Pp. 40–56, 2023.

J. Aurima, S. Susaldi, N. Agustina, A. Masturoh, R. Rahmawati, And M. Tresiana Monika Madhe, “Faktor-Faktor Yang Berhubungan Dengan Kejadian Stunting Pada Balita Di Indonesia,” Open Access Jakarta Journal Of Health Sciences, Vol. 1, No. 2, Pp. 43–48, Nov. 2021, Doi: 10.53801/Oajjhs.V1i3.23.

Y. Deldjoo, T. Di Noia, And F. A. Merra, “A Survey On Adversarial Recommender Systems: From Attack/Defense Strategies To Generative Adversarial Networks,” Acm Computing Surveys (Csur), Vol. 54, No. 2, Pp. 1–38, 2021.

A. Saxena Et Al., “A Review Of Clustering Techniques And Developments,” Neurocomputing, Vol. 267, Pp. 664–681, 2017, Doi: Https://Doi.Org/10.1016/J.Neucom.2017.06.053.

A. Yudhistira And R. Andika, “Pengelompokan Data Nilai Siswa Menggunakan Metode K-Means Clustering,” Journal Of Artificial Intelligence And Technology Information (Jaiti), Vol. 1, No. 1, Pp. 20–28, Feb. 2023, Doi: 10.58602/Jaiti.V1i1.22.

I. Jayaperwira, A. T. Wibowo, And D. Nurjanah, “Anime Rekomendasi Menggunakan Collaborative Filtering,” Eproceedings Of Engineering, Vol. 10, No. 3, 2023.

D. Arthur And S. Vassilvitskii, “K-Means++: The Advantages Of Careful Seeding.”

R. Rahmattullah, Indwiarti, And A. A. Rohmawati, “Clustering Harga Rumah: Perbandingan Model K-Means Dan Gaussian Mixture Model,” E-Proceeding Of Engineering, Vol. 10, No. 3, Pp. 3441–3449, Jun. 2023.

L. Ardiansyah And S. A. Awalludin, “Implementation Of The K-Mean Algorithm To Determine The Level Of Student Satisfaction With The Online Learning Uhamka System (Olu),” Jurnal Pembelajaran Dan Matematika Sigma (Jpms), Vol. 9, No. 1, Pp. 162–171, May 2023, Doi: 10.36987/Jpms.V9i1.4121.

D. Normawati And S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” 2021.




DOI: https://doi.org/10.30645/kesatria.v6i1.565

DOI (PDF): https://doi.org/10.30645/kesatria.v6i1.565.g560

Refbacks

  • There are currently no refbacks.


Published Papers Indexed/Abstracted By: