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      "title": "PERBANDINGAN EFEKTIVITAS MODEL TF-IDF DAN WORD2VEC DALAM\r\nSISTEM REKOMENDASI BUKU BERBASIS CONTENT-BASED",
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      "date": "2026-04-21",
      "abstract": "Secara global, industri penerbitan buku terus mengalami pertumbuhan.\r\nBerdasarkan data UNESCO dan International Publishers Association (IPA), setiap\r\ntahun diterbitkan lebih dari dua juta judul buku baru di seluruh dunia. Di Indonesia\r\nsendiri, menurut data Perpustakaan Nasional (PERPUSNAS) tahun 2020 dan 2021,\r\ntercatat hampir 150.000 buku diterbitkan dalam satu tahun lebih tinggi dibanding\r\n2019 berada di angka 120.000 buku. Peningkatan jumlah publikasi tersebut\r\nmenunjukkan berkembangnya aktivitas penerbitan dan ketersediaan bahan bacaan,\r\nnamun juga menimbulkan tantangan dalam membantu pembaca menemukan buku\r\nyang relevan dengan minat dan kebutuhannya. Dengan adanya sebuah sistem\r\nrekomendasi yang menerapkan berbagai macam algoritma dengan kebutuhan yang\r\nbermacam-macam tentu juga menjadi tantangan lagi bagi para pengelola untuk\r\nmenghasilkan rekomendasi yang dapat disesuaikan dengan para pembaca.\r\nDengan adanya kedua masalah tersebut penelitian ini bertujuan untuk\r\nmembandingkan kinerja antara dua pendekatan algoritma dalam sistem\r\nrekomendasi buku, yaitu Word2Vec dan Cosine Similarity dengan metode ContentBased\r\n\r\nFiltering. Kedua metode ini memiliki karakteristik yang berbeda dalam\r\nmenghasilkan sebuah rekomendasi, namun keduanya tetaplah memiliki kebutuhan\r\ndata yang minim dengan hanya memerlukan data dari konten atau buku itu sendiri.\r\nMaka dari itu pengujian ini dilakukan dengan menggunakan dataset buku yang\r\nmencakup informasi seperti judul, penulis, kategori, dan rating pembaca. Melalui\r\nperbandingan ini diharapkan dapat diperoleh pemahaman mengenai algoritma yang\r\nlebih efektif dalam meningkatkan relevansi rekomendasi serta memberikan\r\nkontribusi terhadap pengembangan sistem rekomendasi yang cocok dengan\r\npembaca maupun pengguna sistem hingga penelitian kedepannya.",
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