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      "abstract": "Fenomena information overload pada industri perfilman digital saat ini \r\nmengakibatkan pengguna mengalami kesulitan dalam menentukan pilihan tontonan\r\nyang relevan, yang berdampak pada penurunan efisiensi waktu dan kepuasan\r\n3rekomendasi film yang mampu mengurai kompleksitas tersebut dengan\r\nmenerapkan tiga metode komparatif, yaitu Demographic Filtering, Content-Based\r\nFiltering, dan Collaborative Filtering berbasis algoritma Singular Value\r\nDecomposition (SVD). Implementasi sistem dilakukan menggunakan arsitektur\r\nmicroservices dengan mengintegrasikan pangkalan data MovieLens dan TMDB.\r\nEvaluasi kualitas performa sistem tidak hanya diukur melalui metrik statistik\r\nRMSE dan MAE, tetapi juga melalui parameter usulan Recommendation Quality\r\nScore (RQS) yang menggabungkan aspek akurasi, personalisasi, dan diversitas\r\nsecara proporsional. Hasil penelitian menunjukkan bahwa algoritma SVD\r\nmerupakan metode paling superior dengan capaian skor RQS tertinggi sebesar\r\n80,78 dibandingkan metode lainnya. Sistem ini berhasil memberikan rekomendasi\r\nyang presisi namun tetap variatif bagi pengguna guna memitigasi efek filter bubble.\r\nKontribusi penelitian ini dapat dimanfaatkan oleh pengembang platform streaming\r\nvideo untuk meningkatkan retensi pengguna melalui personalisasi konten yang\r\nlebih cerdas. Penelitian lebih lanjut direkomendasikan untuk melakukan kalibrasi\r\nbobot heuristik RQS melalui pengujian langsung kepada responden riil atau\r\npenggunaan algoritma deep learning untuk menangani skala data yang lebih masif."
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