Identifikasi Cyberbullying Pada Komentar Instagram Menggunakan Metode Lexicon-based Dan Naïve Bayes Classifier (studi Kasus: Pemilihan Presiden Indonesia Tahun 2019) - Rizky Dhian Syarif; Anisa Herdiani; Widi Astuti (eProceedings of Engineering, 2019)
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 ramaidiperbincangkan di dunia nyata maupun dunia maya, khususnya di media sosial Instagram. Semua orangbebas berpendapat atau beropini tentang masing-masing calon Presiden. Tetapi, yang menjadi persoalanadalah ketika berpendapat tidak berlandaskan etika, sehingga membuat pertentangan antaramasingmasing pendukung pasangan calon presiden. Perang komentar yang membully, menjelekkan, ataumenjatuhkan lawan mewarnai situasi tersebut. Untuk itu, perlu dilakukan identifikasi cyberbullying padakomentar Instagram untuk mengklasifikasikan komentar yang mengandung cyberbullying atau noncyberbullying. Metode yang digunakan dalam penelitian ini adalah metode berbasis lexicon dan metodeberbasis learning yaitu naïve bayes classifier. Proses sistem dimulai dari text preprocessing dengan tahapancleaning, casefolding, dan stemming. Kemudian dilakukan proses klasifikasi menggunakan metode Lexiconbased dan naïve bayes classifier, dan hasil keluaran sistem berupa identifikasi apakah komentar termasukcyberbullying atau non cyberbullying. Pada penelitian ini didapatkan hasil performansi dari metode LexiconBasedmenghasilkanakurasisebesar58%,presisi52%,recall75%danF-score61%.Sedangkannaïvebayesclassifierdidapatkanakurasi97%,presisi94%,recall100%,danF1-score97%. Kata kunci : cyberbullying, instagram, Lexicon-Based , naïve bayes classifier. Abstract In 2019 Indonesia was colored with the vibrant democracy. The community welcomed with great enthusiasmand enthusiasm at the Presidential Election held in April 2019. The presidential election was heavilydiscussed in the real world and cyberspace, specifically on Instagram social media. All people are free toapprove or opinion about each candidate for President. However, what is being debated is a compilationthat is not based on ethics, thus creating a conflict between each of the supporters of the presidentialcandidate pair. The war of comments that bully, vilify, or bring down opponents depicts beforehand. Forthis reason, it is necessary to collect cyberbullying on Instagram comments to classify comments that containcyberbullying or non-cyberbullying. The method used in this research is the lexicon based method and theBayes classifier naïve 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 methodand the naïve Bayes classifier, and the output of the system involves commenting whether it is cyberbullyingor non-cyberbullying. In this study the performance results obtained from the Lexicon-Based methodproduce an accuracy of 58%, 52% precision, 75% recall and F-score 61%. While Naïve Bayes Classifierobtained 97% accuracy, 94% precision, 100% recall, and F1-score 97%. Keywords: cyberbullying, instagram, based on lexicon, naive bayes classifier.