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      "keywords": "Data mining, sistem rekomendasi, ide konten, TF-IDF, cosine\r\nsimilarity, recommendation system, content ideas.",
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      "abstract": "Perkembangan pesat media sosial mendorong meningkatnya jumlah content\r\ncreator yang dituntut untuk menghasilkan konten secara konsisten, relevan, dan\r\nsesuai dengan tren yang sedang berkembang. Namun, proses pencarian ide konten\r\nmasih banyak dilakukan secara manual dengan mengamati konten populer,\r\nsehingga memerlukan waktu yang cukup lama dan kurang efisien. Permasalahan\r\ntersebut menunjukkan perlunya sebuah sistem yang mampu membantu content\r\ncreator dalam menghasilkan ide konten secara otomatis dan berbasis data.\r\nPenelitian ini bertujuan untuk membangun sistem rekomendasi ide konten\r\notomatis berbasis website dengan memanfaatkan konsep Data Mining dan\r\nIntelligent Business. Sistem ini menggunakan data konten populer dari platform\r\nmedia sosial dengan indikator engagement berupa jumlah penayangan (views), suka\r\n(likes), bagikan (shares), simpan (saves), jumlah waktu tayang (watchtime). Data\r\nteks berupa caption dan hashtag diolah menggunakan teknik Natural Language\r\nProcessing (NLP) melalui tahapan preprocessing teks. Selanjutnya, representasi\r\nteks dilakukan menggunakan metode Term Frequency–Inverse Document\r\nFrequency (TF-IDF), sedangkan proses rekomendasi ditentukan berdasarkan\r\ntingkat kemiripan menggunakan metode Cosine Similarity.\r\nHasil dari penelitian ini berupa sistem yang mampu memberikan\r\nrekomendasi ide konten yang relevan, lengkap dengan tema, caption, dan hashtag,\r\nserta menyediakan fitur penyimpanan atau favorit rekomendasi. Sistem ini\r\ndiharapkan dapat menjadi alat bantu pengambilan keputusan bagi content creator\r\ndalam menentukan ide konten yang sesuai dengan tren dan memiliki potensi\r\nengagement tinggi.",
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      "institution": "Universitas AMIKOM Yogyakarta",
      "divisions": [
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      "title": "SISTEM REKOMENDASI IDE KONTEN OTOMATIS UNTUK CONTENT\r\nCREATOR BERBASIS TREN DAN ENGAGEMENT KONTEN\r\nMENGGUNAKAN TF-IDF DAN COSINE SIMILARITY",
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