Identifikasi Cyberbullying Pada Komentar Instagram Menggunakan Metode Lexicon-based Dan Naïve Bayes Classifier (studi Kasus: Pemilihan Presiden Indonesia Tahun 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 naïve bayes classifier. Proses sistem dimulai dari text preprocessing dengan tahapan
cleaning, casefolding, dan stemming. Kemudian dilakukan proses klasifikasi menggunakan metode Lexicon
based dan naïve 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
naïve
bayes

classifier
didapatkan
akurasi
97%,
presisi
94%,
recall
100%,
dan
F1-score
97%.

 

 

Kata kunci : cyberbullying, instagram, Lexicon-Based , naïve 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 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 method
and the naïve 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 Naïve Bayes Classifier
obtained 97% accuracy, 94% precision, 100% recall, and F1-score 97%.

Keywords: cyberbullying, instagram, based on lexicon, naive bayes classifier.