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      "abstract": "Perkembangan industri game saat ini berkembang dengan cepat, di mana\r\nsetiap tahunnya muncul ribuan judul game baru, hal ini menimbulkan permasalahan\r\nbagi para konsumen game, di mana dengan munculnya banyak judul game baru,\r\nmembuat mereka kesulitan dalam memilih game apa yang ingin mereka mainkan,\r\nditambah tidak sedikit game yang memiliki harga yang tinggi, sehingga menambah\r\nvariabel yang membuat pengguna tidak bisa memainkan game yang mereka\r\ninginkan. Untuk mengatasi hal ini dibutuhkan sebuah sistem rekomendasi yang\r\ndapat memberikan saran judul game yang mirip dengan game yang diinginkan oleh\r\npengguna.\r\nPenelitian ini menggunakan metode content-based filtering dengan\r\npendekatan cosine similarity. metode ini dipilih karena fokus utama adalah mencari\r\ngame semirip mungkin dengan konten atau atribut game, seperti genre, tag, dan\r\ndeskripsi tanpa harus bergantung pada ulasan pengguna lain.\r\nFokus penelitian ini adalah membangun sebuah sistem rekomendasi yang\r\ndapat menemukan game dengan tingkat kemiripan yang tinggi dengan game yang\r\ndicari oleh pengguna.",
      "lastmod": "2026-09-24 03:34:36",
      "date": "2026-05-21",
      "department": "Fakultas Ilmu Komputer",
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      "keywords": "Sistem Rekomendasi, Game, Content-Based Filtering, Cosine\r\nSimilarity, Steam, Recommendation System.",
      "thesis_type": "bachelor",
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