Analisis dan Implementasi Singular Value Decomposition (SVD) untuk Sistem Rekomendasi Anime Berbasis Collaborative Filtering
DOI:
https://doi.org/10.30865/jurikom.v13i1.9462Keywords:
Recommendation System, Anime, Singular Value Decomposition, Truncated SVD, Precision@NAbstract
The rapid growth of digital platforms has significantly increased the number of available anime titles, making it difficult for users to choose content that matches their preferences. In addition, the diversity of genres and content characteristics accessible to different age groups highlights the need for a system that can provide recommendations more accurately and efficiently. This study aims to develop an anime recommendation system based on collaborative filtering by applying the truncated Singular Value Decomposition (SVD) method to address the data sparsity problem in the user–item matrix. The dataset was collected from the AniList platform and consists of 30 users, 100 anime titles, and 1,230 explicit rating records. The evaluation was conducted using a hold-out scheme with an 80% training set (992 ratings) and a 20% testing set (248 ratings). Prediction performance was measured using an RMSE of 1.6464 and an MAE of 1.2370, while the quality of Top-N recommendations was assessed using Precision@10. The experimental results indicate that the best configuration was achieved at k = 4, which produced the lowest prediction error on the test set and generated relevant recommendations. The average Precision@10 obtained was 0.1897, meaning that approximately 18.97% of the Top-10 recommendations provided by the system were considered relevant.
References
[1] D. Kurniawan Salamoon, “Anime Sebagai Media Edukasi Digital Mengenai Fungsi Sel Darah Merah (Analisis Visualisasi Karakter AE 3803 Pada Anime Hataraku Saibou) Anime as a Digital Educational Media Regarding the Function of Red Blood Cells (AE 3803 Character Visualization Analysis in Anime Hataraku Saibou),” Jurnal Seni Budaya, vol. 36, no. 2, pp. 197–203, 2021.
[2] A. Nurmana and Florencia Irene Yuliani, “anime Pengaruh Anime (kartun Jepang) Terhadap Perilaku Kekarasan Pada Remaja,” Pixel :Jurnal Ilmiah Komputer Grafis, vol. 17, no. 2, pp. 67–76, Dec. 2024, doi: 10.51903/pixel.v17i2.2107.
[3] W. Ferbiansyah, A. Muhammad Irwan, B. Santoso, and S. Kacung, “IMPLEMENTASI METODE CONTENT BASED FILTERING MENGGUNAKAN SYNOPSIS SIMILARITY UNTUK PEMILIHAN ANIME,” Jurnal Informatika Teknologi dan Sains, vol. 7, no. 2, p. 955, 2025.
[4] Rosita, “REKOMENDASI BUKU PERPUSTAKAAN KAMPUS DENGAN METODE ITEM-BASED COLLABORATIVE FILTERING,” Sebatik, vol. 26, no. 1, pp. 340–346, Jun. 2022, doi: 10.46984/sebatik.v26i1.1551.
[5] N. Winnie, “ALiterature Review of Collaborative Filtering Recommendation System using Matrix Factorization algorithms,” in Proceedings of ACM Conference (Conference’17), IEEE Computer Society, 2022, pp. 36–47. doi: 10.1145/nnnnnnn.nnnnnnn.
[6] I. K. G. Aryadi Pramarta and Z. K. A. Baizal, “HYBRID RECOMMENDER SYSTEM USING SINGULAR VALUE DECOMPOSITION AND SUPPORT VECTOR MACHINE IN BALI TOURISM,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) , vol. 07, no. 02, 2022.
[7] W. Shalannanda, R. F. Mulia, A. I. Muttaqien, N. R. Hibatullah, and A. Firdaus, “Singular value decomposition model application for e-commerce recommendation system,” JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga), vol. 2, no. 2, pp. 103–110, Sep. 2022, doi: 10.35313/jitel.v2.i2.2022.103-110.
[8] M. E. Ramadhan and A. T. Wibowo, “Music Recommender System Based on Play Count Using Singular Value Decomposition++,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 7, no. 3, p. 1210, Jul. 2023, doi: 10.30865/mib.v7i3.6424.
[9] M. Attalariq and Z. K. A. Baizal, “Chatbot-Based Book Recommender System Using Singular Value Decomposition,” Journal of Information System Research (JOSH), vol. 4, no. 4, pp. 1293–1301, Jul. 2023, doi: 10.47065/josh.v4i4.3817.
[10] F. Colace et al., “Recommender systems: a novel approach based on singular value decomposition,” International Journal of Electrical and Computer Engineering, vol. 12, no. 6, pp. 6513–6521, Dec. 2022, doi: 10.11591/ijece.v12i6.pp6513-6521.
[11] R. Siringoringo and G. Lumbantoruan, “SISTEM PEREKOMENDASI DENGAN SINGULAR VALUE DECOMPOSITION DAN TEKNIK SIMILARITAS PEARSON CORRELATION,” Jurnal METHODIKA, vol. 7, no. 1, 2021, [Online]. Available: http://www2.informatik.uni-freiburg.de/~cziegler/BX/.
[12] W. Andriyani et al., PERANGKAT LUNAK DATA MINING. Penerbit Widina, 2024. [Online]. Available: https://books.google.co.id/books?id=bdouEQAAQBAJ
[13] I. Zuhdiansyah and A. Luthfiarta, “Sistem Rekomendasi Pembelian Smartphone berbasis Algoritma K-Means dan Singular Value Decomposition,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 10, no. 1, pp. 45–53, May 2024, doi: 10.25077/teknosi.v10i1.2024.45-53.
[14] F. Nissa, A. H. Primandari, and A. K. Thalib, “COLLABORATIVE FILTERING APPROACH: SKINCARE PRODUCT RECOMMENDATION USING SINGULAR VALUE DECOMPOSITION (SVD),” MEDIA STATISTIKA, vol. 15, no. 2, pp. 139–150, Apr. 2023, doi: 10.14710/medstat.15.2.139-150.
[15] Z. T. Ke and M. Wang, “Using SVD for Topic Modeling,” J Am Stat Assoc, vol. 119, no. 545, pp. 434–449, 2024, doi: 10.1080/01621459.2022.2123813.
[16] R. Alabduljabbar, “Matrix Factorization Collaborative-Based Recommender System for Riyadh Restaurants: Leveraging Machine Learning to Enhance Consumer Choice,” Applied Sciences (Switzerland), vol. 13, no. 17, Sep. 2023, doi: 10.3390/app13179574.
[17] K. K. Jena et al., “E-Learning Course Recommender System Using Collaborative Filtering Models,” Electronics (Switzerland), vol. 12, no. 1, Jan. 2023, doi: 10.3390/electronics12010157.
[18] A. abbas, A. S. T. J. S. Manish Jaiswal, “Hybrid Recommender System based on Singular value decomposition using content features,” NeuroQuantology, vol. 20, no. 9, Jun. 2024, doi: 10.48047/nq.2022.20.9.nq44485.
[19] S. L. Brunton and J. N. Kutz, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2022. [Online]. Available: https://books.google.co.id/books?id=rxNkEAAAQBAJ
[20] A. Falini, “A review on the selection criteria for the truncated SVD in Data Science applications,” Journal of Computational Mathematics and Data Science, vol. 5, Dec. 2022, doi: 10.1016/j.jcmds.2022.100064.
[21] C. Wibisono, L. S. Haryadi, J. E. Widyaya, and S. L. Liliawati, “Sistem Rekomendasi Suku Cadang Berdasarkan Item Based Filtering,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 7, no. 1, Apr. 2021, doi: 10.28932/jutisi.v7i1.3036.
[22] T. A. W. Tyas, Z. K. A. Baizal, and R. Dharayani, “Tourist Places Recommender System Using Cosine Similarity and Singular Value Decomposition Methods,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 5, no. 4, p. 1201, Oct. 2021, doi: 10.30865/mib.v5i4.3151.
[23] R. Buana Perdana, D. Hartanti, and H. Hasanah, “Sistem Rekomendasi Peminjaman Buku Menggunakan Metode Vector Space Model Berbasis Pembobotan TF-IDF dan FastText (Studi Kasus Perpustakaan SMP Negeri 1 Kartasura),” Infotek: Jurnal Informatika dan Teknologi, vol. 8, no. 2, pp. 391–401, Jul. 2025, doi: 10.29408/jit.v8i2.30540.
