ROZAQI, SADDAM WAHIB (2021) ANALISIS SENTIMEN PADA SOSIAL MEDIA MENGGUNAKAN METODE NAÏVE BAYES. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Technological developments in cyberspace can be a reliable research subject in obtaining information, because of the large amount of text data found on social media. In recent years, there has been an increase in research into sentiment analysis in text reviews and tweets in order to determine the polarity generated by social media. This study aims to analyze positive and negative emotions from the data that has been obtained from social media, using the information classification approach in the text and dividing it into positive and negative classes using the Naïve Bayes method. The dataset is directly taken and collected from users' posts on social media. In testing the Naïve Bayes method, the calculation of the accuracy, exactness, review, f-measure values is generated. The results of the Naïve Bayes method of processing show quite well with the average value of the multiclass getting 92%. Key words: sentiment analysis, classification, naïve bayes, social media, positive, negative Perkembangan teknologi di dunia maya dapat menjadi subjek penelitian yang bisa diandalkan dalam memperoleh informasi, karena banyaknya data teks yang terdapat pada sosial media. Dalam beberapa tahun terakhir, telah terjadi peningkatan penelitian terhadap analisis sentimen pada teks ulasan maupun cuitan agar dapat mengetahui polaritas yang dihasilkan dari sosial media. Penelitian ini bertujuan untuk menganalisa emosi bersifat positif maupun negatif dari data yang telah didapatkan dari sosial media, menggunakan pendekatan klasifikasi informasi yang ada pada teks dan membaginya menjadi kelas positif dan negatif dengan menggunakan metode Naïve Bayes. Dataset langsung diambil dan dikumpulkan dari tulisan pengguna pada media sosial. Dalam pengujian metode Naïve Bayes ini dihasilkan perhitungan dari nilai akurasi, exactness, review, f-measure. Pada hasil pengolahan metode Naïve Bayes menunjukan cukup baik dengan nilai rata-rata dari multiclass mendapatkan hasil 92%. Kata kunci: analisis sentimen, klasifikasi, naïve bayes, sosial media, positif, negatif.
| Item Type: | Thesis (S1) |
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| NIM/NIDN Creators: | 41516120084 |
| Uncontrolled Keywords: | analisis sentimen, klasifikasi, naïve bayes, sosial media, positif, negatif. |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | Dede Muksin Lubis |
| Date Deposited: | 02 Nov 2023 03:19 |
| Last Modified: | 02 Nov 2023 03:19 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/83703 |
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