Website Rekomendasi Anime Dengan Menggunakan Pendekatan Content-Based Filtering Berdasarkan Sinopsis
Abstract
Today there are various kinds of Anime titles that can be found to be able to provide appropriate recommendations to Anime fans. A recommendation system is designed that can provide recommendations for Anime titles based on the similarity of the synopsis that has been read by users. The design of this Recommendation System uses a Content Based Filtering approach using the TFIDF method to obtain features that represent each synopsis which then produces a TFIDF matrix. The TFIDF matrix is then used as input to calculate the degree of similarity between synopsis. The results of the calculation of the degree of similarity become the basis for the formation of a function that can provide recommendations for Anime titles to users.
References
[1]F. Nugroho and M. Ismu Rahayu, “SISTEM REKOMENDASI PRODUK UKM DI KOTA BANDUNG MENGGUNAKAN ALGORITMA COLLABORATIVE FILTERING,” Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI), vol. 2, no. 3, pp. 23–31, Sep. 2020, doi: 10.52005/jursistekni.v2i3.63.
[2]A. Wijaya and D. Alfian, “Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering,” Jurnal Computech & Bisnis (e-Journal), vol. 12, no. 1, pp. 11–27, Jun. 2018, doi: 10.55281/jcb.v12i1.167.
[3]M. Nurjannah, H. Hamdani, and I. F. Astuti, “PENERAPAN ALGORITMA TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) UNTUK TEXT MINING,” Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, vol. 8, no. 3, pp. 110–113, Jun. 2016, doi: 10.30872/jim.v8i3.113.
[4]B. Herwijayanti, D. E. Ratnawati, and L. Muflikhah, “Klasifikasi Berita Online dengan menggunakan Pembobotan TF-IDF dan Cosine Similarity,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 1, pp. 306–312, Aug. 2017.
[5]Y. K. Ng, “CBRec: a book recommendation system for children using the matrix factorisation and content-based filtering approaches,” International Journal of Business Intelligence and Data Mining, vol. 16, no. 2, p. 129, 2020, doi: 10.1504/ijbidm.2020.104738.
[6]J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decision Support Systems, vol. 74, pp. 12–32, Jun. 2015, doi: 10.1016/j.dss.2015.03.008.
[2]A. Wijaya and D. Alfian, “Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering,” Jurnal Computech & Bisnis (e-Journal), vol. 12, no. 1, pp. 11–27, Jun. 2018, doi: 10.55281/jcb.v12i1.167.
[3]M. Nurjannah, H. Hamdani, and I. F. Astuti, “PENERAPAN ALGORITMA TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) UNTUK TEXT MINING,” Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer, vol. 8, no. 3, pp. 110–113, Jun. 2016, doi: 10.30872/jim.v8i3.113.
[4]B. Herwijayanti, D. E. Ratnawati, and L. Muflikhah, “Klasifikasi Berita Online dengan menggunakan Pembobotan TF-IDF dan Cosine Similarity,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 1, pp. 306–312, Aug. 2017.
[5]Y. K. Ng, “CBRec: a book recommendation system for children using the matrix factorisation and content-based filtering approaches,” International Journal of Business Intelligence and Data Mining, vol. 16, no. 2, p. 129, 2020, doi: 10.1504/ijbidm.2020.104738.
[6]J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decision Support Systems, vol. 74, pp. 12–32, Jun. 2015, doi: 10.1016/j.dss.2015.03.008.
Published
2022-11-25
How to Cite
SUHENDRA, Arfal Razya; PUTRA, ST., M.CS, I Gusti Ngurah Anom Cahyadi.
Website Rekomendasi Anime Dengan Menggunakan Pendekatan Content-Based Filtering Berdasarkan Sinopsis.
Jurnal Nasional Teknologi Informasi dan Aplikasnya, [S.l.], v. 1, n. 1, p. 451-458, nov. 2022.
ISSN 3032-1948.
Available at: <https://ojs.unud.ac.id/index.php/jnatia/article/view/92538>. Date accessed: 30 jan. 2025.
Section
Articles
