<> <http://www.w3.org/2000/01/rdf-schema#comment> "The repository administrator has not yet configured an RDF license."^^<http://www.w3.org/2001/XMLSchema#string> .
<> <http://xmlns.com/foaf/0.1/primaryTopic> <http://katalog.ukdw.ac.id/id/eprint/2508> .
<http://katalog.ukdw.ac.id/id/eprint/2508> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/ontology/bibo/Thesis> .
<http://katalog.ukdw.ac.id/id/eprint/2508> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/ontology/bibo/Article> .
<http://katalog.ukdw.ac.id/id/eprint/2508> <http://purl.org/dc/terms/title> "IMPLEMENTASI ALGORITMA IB1  TERHADAP KLASIFIKASI SENTIMEN PADA REVIEW FILM"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://katalog.ukdw.ac.id/id/eprint/2508> <http://purl.org/ontology/bibo/abstract> "Review dapat digunakan dalam dunia perfilman sebagai pertimbangan untuk menentukan apakah sebuah film akan ditonton atau tidak. Dengan semakin banyaknya film yang dirilis, maka semakin banyak pula review penonton maupun pengamat film yang beredar di situs-situs perfilman. Kebanyakan review tidak dikategorikan berdasarkan isinya secara eksplisit apakah tergolong dalam review yang positif atau negatif. Akibatnya, pembaca review harus menentukan sendiri apakah review yang dibacanya bersifat positif atau negatif. \r\nAdanya klasifikasi sentimen untuk review sebuah film akan memberi kemudahan bagi pengguna dalam mencari jenis review yang diinginkan. Adapun penelitian ini penulis menggunakan algoritma IB1 dimana teks direpresentasikan dalam vektor dengan menggunakan metode TF-IDF untuk pembobotan, serta komponen WordNet untuk mengekspansi token dengan sinonim nya. Setiap dokumen uji akan dihitung jarak kemiripannya dengan setiap dokumen latih menggunakan cosine similarity. Pemilihan kelas dilakukan berdasarkan similar instances (atau jumlah k dalam k-NN) yang dipakai. Dokumen uji yang terklasifikasi dengan benar akan dimasukan dalam kumpulan dokumen latih.\r\nDalam penelitian ini penulis melakukan beberapa pengujian evaluasi untuk mengetahui efektivitas klasifikasi algoritma IB1 dengan atau tanpa WordNet. Sistem klasifikasi yang dikembangkan dapat memberikan akurasi yang cukup tinggi. Rata-rata akurasi tanpa menggunakan WordNet sebesar 72,9%\r\nsedangkan rata-rata akurasi dengan menggunakan WordNet menghasilkan nilai sebesar 78,7%. Nilai rata-rata F-Measure yaitu 0,719 untuk klasifikasi tanpa WordNet dan 0.772 untuk klasifikasi menggunakan WordNet"^^<http://www.w3.org/2001/XMLSchema#string> .
<http://katalog.ukdw.ac.id/id/eprint/2508> <http://purl.org/dc/terms/date> "2015-08" .
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<http://katalog.ukdw.ac.id/id/document/9656> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://eprints.org/ontology/Document> .
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