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      "title": "SISTEM REKOMENDASI ANIME MENGGUNAKAN\r\nCONTENT-BASED FILTERING DENGAN ALGORITMA\r\nCOSINE SIMILARITY",
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      "eprintid": 32979,
      "abstract": "Perkembangan industri anime yang sedang berkembang dengan pesat\r\nmenyebabkan jumlah judul anime meningkat secara signifikan di setiap tahunya.\r\nHal ini membuat para penonton mengalami kesulitan dalam menemukan anime\r\nyang sesuai dengan preferensi mereka. Untuk mengatasi permasalahan tersebut\r\ndiperlukan sebuah sistem rekomendasi yang dapat memberikan saran anime yang\r\nmenggunakan content based filtering dengan algoritma cosine similarity. Metode\r\nini bekerja dengan menganalisis kemiripan antar anime berdasarkan fitur seperti\r\ngenre, sinopsis, dan tipe anime.  Proses perhitungan kemiripan dilakukan dengan\r\nrepresentasi data menggunakan term frequency-inverse document frequency\r\n(TF-IDF) dan dilanjutkan dengan pengukuran derajat menggunakan cosine\r\nsimilarity. Pendekatan serupa telah banyak digunakan dalam berbagai bidang\r\npenelitian dan terbukti efektif dalam menghasilkan rekomendasi yang relevan,\r\nmisalnya sistem rekomendasi buku di lingkungan perpustakaan sekolah, sistem\r\nrekomendasi artikel berita bagi mahasiswa, sistem rekomendasi musik berbasis\r\nlirik lagu, sistem rekomendasi film berdasarkan sinopsis, serta sistem\r\nrekomendasi kuliner untuk membantu konsumen memilih menu. Semua penelitian\r\ntersebut menunjukan bahwa metode content-based filtering yang dikombinasikan\r\ndengan TF-IDF dan cosine similarity mampu memberikan hasil yang akurat,\r\nrelevan dan mudah diimplementasikan. Dengan demikian, penelitian ini\r\ndiharapkan mampu memberikan rekomendasi anime yang relevan dengan\r\npreferensi pengguna berdasarkan tingkat kemiripan konten, sehingga sistem dapat\r\nmembantu pengguna dalam menemukan anime baru sesuai dengan minat mereka\r\nsecara efisien.",
      "lastmod": "2026-09-24 03:27:10",
      "date": "2026-04-20"
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