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      "keywords": "Sistem Rekomendasi, Content-Based Filtering, TF-IDF, Cosine\r\nSimilarity, Film, Recommendation System, Movie.",
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      "institution": "Universitas AMIKOM Yogyakarta",
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      "abstract": "Perkembangan pesat platform streaming film di era digital telah\r\nmenyebabkan lonjakan volume konten yang masif, sehingga memicu fenomena\r\ninformation overload. Kondisi ini berdampak negatif pada pengalaman pengguna,\r\nmengakibatkan kelelahan dalam pengambilan keputusan (decision fatigue) dan\r\ninefisiensi waktu dalam menemukan tontonan yang relevan dengan preferensi\r\nmereka. Penelitian ini bertujuan mengatasi permasalahan tersebut dengan\r\nmembangun sebuah sistem rekomendasi film menggunakan metode Content-Based\r\nFiltering. Metodologi penyelesaian masalah dilakukan melalui beberapa tahapan\r\nsistematis, meliputi akuisisi dataset film, pra-pemrosesan data teks, rekayasa fitur\r\ngabungan (soup) yang mencakup genre dan sinopsis, pembobotan fitur\r\nmenggunakan Term Frequency-Inverse Document Frequency (TF-IDF), serta\r\nperhitungan tingkat kemiripan antar dokumen menggunakan algoritma Cosine\r\nSimilarity. Sistem yang dirancang kemudian diimplementasikan ke dalam sebuah\r\nprototipe aplikasi berbasis web fungsional. Hasil akhir penelitian menunjukkan\r\nbahwa sistem mampu memberikan rekomendasi yang akurat dan relevan,\r\ndibuktikan dengan evaluasi kinerja yang mencapai persentase kesamaan genre\r\nhingga 80% dan kesamaan kru film sebesar 70% pada skenario pengujian.\r\nPenelitian ini memberikan manfaat praktis bagi pengguna dalam mempercepat\r\npencarian film, serta menjadi rujukan bagi penyedia layanan hiburan untuk\r\nmeningkatkan keterlibatan pengguna melalui fitur personalisasi. Pengembangan\r\nselanjutnya disarankan untuk mengintegrasikan metode Hybrid Filtering guna\r\nmemperkaya variasi rekomendasi.",
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      "title": "IMPLEMENTASI METODE CONTENT BASED FILTERING PADA\r\nSISTEM REKOMENDASI FILM",
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