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Identifikasi Cyberbullying Pada Komentar Instagram Menggunakan Metode Lexicon-based Dan Naïve Bayes Classifier (studi Kasus: Pemilihan Presiden Indonesia Tahun 2019)
| Content Provider | Semantic Scholar |
|---|---|
| Author | Syarif, Rizky Dhian |
| Copyright Year | 2019 |
| Abstract | Abstrak Tahun 2019 Indonesia diwarnai dengan semarak demokrasi. Masyarakat menyambut dengan gembira dan antusiasme yang tinggi pada Pemilihan Umum Presiden yang dilaksanakan April 2019. Pilpres ini ramai diperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Instagram. Semua orang bebas berpendapat atau beropini tentang masing-masing calon Presiden. Tetapi, yang menjadi persoalan adalah ketika berpendapat tidak berlandaskan etika, sehingga membuat pertentangan antara masingmasing pendukung pasangan calon presiden. Perang komentar yang membully, menjelekkan, atau menjatuhkan lawan mewarnai situasi tersebut. Untuk itu, perlu dilakukan identifikasi cyberbullying pada komentar Instagram untuk mengklasifikasikan komentar yang mengandung cyberbullying atau non cyberbullying. Metode yang digunakan dalam penelitian ini adalah metode berbasis lexicon dan metode berbasis learning yaitu naive bayes classifier. Proses sistem dimulai dari text preprocessing dengan tahapan cleaning, casefolding, dan stemming. Kemudian dilakukan proses klasifikasi menggunakan metode Lexicon based dan naive bayes classifier, dan hasil keluaran sistem berupa identifikasi apakah komentar termasuk cyberbullying atau non cyberbullying. Pada penelitian ini didapatkan hasil performansi dari metode LexiconBased menghasilkan akurasi sebesar 58%, presisi 52%, recall 75% dan F-score 61%. Sedangkan naive bayes classifier didapatkan akurasi 97%, presisi 94%, recall 100%, dan F1-score 97%. Kata kunci : cyberbullying, instagram, Lexicon-Based , naive bayes classifier. Abstract In 2019 Indonesia was colored with the vibrant democracy. The community welcomed with great enthusiasm and enthusiasm at the Presidential Election held in April 2019. The presidential election was heavily discussed in the real world and cyberspace, specifically on Instagram social media. All people are free to approve or opinion about each candidate for President. However, what is being debated is a compilation that is not based on ethics, thus creating a conflict between each of the supporters of the presidential candidate pair. The war of comments that bully, vilify, or bring down opponents depicts beforehand. For this reason, it is necessary to collect cyberbullying on Instagram comments to classify comments that contain cyberbullying or non-cyberbullying. The method used in this research is the lexicon based method and the Bayes classifier naive learning method. The system process starts from preprocessing text with cleaning, casefolding, and stemming. Then the classification process is carried out using the Lexicon-based method and the naive Bayes classifier, and the output of the system involves commenting whether it is cyberbullying or non-cyberbullying. In this study the performance results obtained from the Lexicon-Based method produce an accuracy of 58%, 52% precision, 75% recall and F-score 61%. While Naive Bayes Classifier obtained 97% accuracy, 94% precision, 100% recall, and F1-score 97%. Keywords: cyberbullying, instagram, based on lexicon, naive bayes classifier. |
| File Format | PDF HTM / HTML |
| Volume Number | 6 |
| Alternate Webpage(s) | https://libraryeproceeding.telkomuniversity.ac.id/index.php/engineering/article/download/9876/9735 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
