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Prananda Antinasari
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Analisis Sentimen Tentang Opini Film Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes Dengan Perbaikan Kata Tidak Baku Prananda Antinasari; Rizal Setya Perdana; Mochammad Ali Fauzi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 12 (2017): Desember 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The rapid growth of social media does not make Twitter left by its users. Twitter is one of the social media that allows user to interact each other, share information, or even to express feelings and opinions, including in expressing film opinions. Comments or Tweets about movies that exist on Twitter can be used as an evaluation in watching movies and increasing film production. To figure it out, sentiment analysis can be used to classify into negative or positive sentiments. In Tweets contain many languages ​​used in the form of non-standard languages ​​such as slang, word-outs, and misspellings. Therefore it takes special handling on Twitter comments. In this research used non-standard word dictionary and Levenshtein Distance normalization to improve non-standard word to standard word by classification Naive Bayes. Based on the result of the test, the highest accuracy, precision, recall, and f-measure value are 98.33%, 96.77%, 100%, and 98.36%.
Co-Authors M. Ali Fauzi Rizal Setya Perdana