Ekspansi Fitur Dengan Fasttext Pada Klasifikasi Topik Menggunakan Metode Naïve Bayes-support Vector Machine(nbsvm) Di Twitter
DOI:
https://doi.org/10.34818/eoe.v9i3.17989Abstract
Abstrak Banyak informasi yang tersebar di berbagai jejaring sosial online, termasuk Twitter. Di Twitter, Banyak orang yang berbagi informasi di sekitarnya. Akan tetapi, Twitter hanya dapat mengirim tweet sebanyak 280 karakter. Oleh karena itu, banyak pengguna yang melakukan pemotongan/penyingkatan kata dan juga penggunaan variasi kata pada tweet agar pada setiap tweet mencakup banyak informasi. Penggunaan variasi kata seperti emoticon, bahasa gaul, dan singkatan pada tweet membuat ketidakcocokan kosa kata dan kalimat yang disampaikan sehingga sulit untuk dimengerti. Dalam hal ini, penulis melakukan ekspansi fitur untuk kasifikasi topik di Twitter agar dapat mengatasi permasalahan tersebut. Metode yang digunakan untuk ekspansi fitur adalah FastText dan metode yang digunakan dalam klasifikasi adalah Naïve Bayes-Support Vector Machine (NBSVM). Hasil dari penelitian ini menunjukkan bahwa sistem klasifikasi topik dengan ekspansi fitur FastText menggunakan metode NBSVM memiliki akurasi sebesar 82.01%. Kata kunci : klasifikasi, NBSVM, fasttext, ekspansi fiturReferences
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