Deteksi Komentar Cyberbullying pada Media Sosial Instagram Menggunakan Algoritma Random Forest
Abstract
Cyberbullying is a phenomenon on social media where technological devices are used to insult, demean, and disrespect others. This can cause mental disorders such as loss of self-confidence, stress, depression, and even suicidal tendencies. The Ditch The Label survey, conducted by a British research institute, identified Instagram as the social media platform with the highest incidence of cyberbullying. The aim of this research is to determine the best accuracy based on the classification results of the cyberbullying comment dataset and to detect new comments as either bullying or non-bullying. One method that can be used is the random forest algorithm, which combines several similar or different methods, such as decision trees, in the classification process. The results of the classification of the testing data using the random forest algorithm show the highest accuracy of 84% in the last hyperparameter tuning combination. The built model can also detect new comments with fairly good predictive results. Suggestions for further research include classifying cyberbullying comments into more specific categories, such as racist or sexist comments.
References
APJII, “Laporan Survei Internet APJII 2019 – 2020,” Asos. Penyelenggara Jasa Internet Indones., vol. 2020, pp. 1–146, 2020, [Online]. Available: https://apjii.or.id/survei.
A. Perwira, J. Dwitama, and K. Kunci, “Deteksi Ujaran Kebencian Pada Twitter Bahasa Indonesia Menggunakan Machine Learning : Reviu Literatur,” J. SNATi, vol. 1, no. 1, pp. 31–39, 2021.
R. Yunanto, A. P. Purfini, and A. Prabuwisesa, “Survei Literatur: Deteksi Berita Palsu Menggunakan Pendekatan Deep Learning,” J. Manaj. Inform., vol. 11, no. 2, pp. 118–130, 2021, doi: 10.34010/jamika.v11i2.5362.
A. S. Hutagalung, A. B. P. Negara, and E. E. Pratama, “Aplikasi Pendeteksi Cyberbullying Terhadap Komentar Postingan Media Sosial Instagram dengan Metode Naïve Bayes Classifier Berbasis Website,” J. Sist. dan Teknol. Inf., vol. 9, no. 3, p. 364, 2021, doi: 10.26418/justin.v9i3.44843.
J. Pardede, Y. Miftahuddin, and W. Kahar, “Deteksi Komentar Cyberbullying Pada Media Sosial Berbahasa Inggris Menggunakan Naïve Bayes Classification,” J. Inform., vol. 7, no. 1, 2020, [Online]. Available: http://ejournal.bsi.ac.id/ejurnal/index.php/ji.
H. Junawan and N. Laugu, “Eksistensi Media Sosial,Youtube, Instagram dan Whatsapp Ditengah Pandemi Covid-19 Dikalangan Masyarakat Virtual Indonesia,” Baitul ’Ulum J. Ilmu Perpust. dan Inf., vol. 4, no. 1, pp. 41–57, 2020, doi: 10.30631/baitululum.v4i1.46.
D. Riswanto and R. Marsinun, “Perilaku Cyberbullying Remaja di Media Sosial,” Analitika, vol. 12, no. 2, pp. 98–111, 2020, doi: 10.31289/analitika.v12i2.3704.
D. I. Cahyani, M. Politeknik, N. Lhokseumawe, and A. Pendahuluan, “Cyberbullying Di Media Sosial Dalam Perspektif Al- Qur ’ an,” Ilmu Al-Qur’an dan Tafsir, vol. 1, no. 1, pp. 36–51, 2022.
M. Azhari, Z. Situmorang, and R. Rosnelly, “Perbandingan Akurasi, Recall, dan Presisi Klasifikasi pada Algoritma C4.5, Random Forest, SVM dan Naive Bayes,” J. MEDIA Inform. BUDIDARMA, vol. 5, no. 2, p. 640, Apr. 2021, doi: 10.30865/mib.v5i2.2937.
R. M. Candra and A. Nanda Rozana, “Klasifikasi Komentar Bullying pada Instagram Menggunakan Metode K-Nearest Neighbor,” IT J. Res. Dev., vol. 5, no. 1, pp. 45–52, Jul. 2020, doi: 10.25299/itjrd.2020.vol5(1).4962.
Normah, B. Rifai, S. Vambudi, and R. Maulana, “Analisa Sentimen Perkembangan Vtuber Dengan Metode Support Vector Machine Berbasis SMOTE,” J. Tek. Komput. AMIK BSI, vol. 8, no. 2, pp. 174–180, 2022, doi: 10.31294/jtk.v4i2.
S. Khomsah and Agus Sasmito Aribowo, “Model Text-Preprocessing Komentar Youtube Dalam Bahasa Indonesia,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 4, pp. 648–654, 2020.
T. Nugraha Manoppo and D. Hatta Fudholi, “Deteksi Cyberbullying berdasarkan Unsur Perbuatan Pidana yang Dilanggar dengan Naive Bayes dan Support Vector Machine,” 2021.
R. Riyaddulloh and A. Romadhony, “Normalisasi Teks Bahasa Indonesia Berbasis Kamus Slang Studi Kasus: Tweet Produk Gadget Pada Twitter,” eProceedings Eng., vol. 8, no. 4, pp. 4216–4228, 2021,[Online].Available:https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/15246/14969.
H. Utama and A. Masruro, “Analisis Sentimen pada Twitter Menggunakan Word Embedding dengan Pendekatan Word2Vec,” J. Sist. Cerdas, vol. 05, no. 02, pp. 128–134, 2022.
A. Nurdin, B. Anggo Seno Aji, A. Bustamin, and Z. Abidin, “Perbandingan Kinerja Word Embedding Word2Vec, Glove, Dan Fasttext Pada Klasifikasi Teks,” J. Tekno Kompak, vol. 14, no. 2, p. 74, 2020, doi: 10.33365/jtk.v14i2.732.
P. Purwono, A. Wirasto, and K. Nisa, “Comparison of Machine Learning Algorithms for Classification of Drug Groups,” Sisfotenika, vol. 11, no. 2, p. 196, 2021, doi: 10.30700/jst.v11i2.1134.
A. U. Zailani and N. L. Hanun, “Penerapan Algoritma Klasifikasi Random Forest Untuk Penentuan Kelayakan Pemberian Kredit Di Koperasi Mitra Sejahtera,” Infotech J. Technol. Inf., vol. 6, no. 1, pp. 7–14, 2020, doi: 10.37365/it.v6i1.61.
S. Budiman, A. Sunyoto, and A. Nasiri, “Analisa Performa Penggunaan Feature Selection untuk Mendeteksi Intrusion Detection Systems dengan Algoritma Random Forest Classifier,” Sistemasi, vol. 10, no. 3, p. 753, 2021, doi: 10.32520/stmsi.v10i3.1550.
I. K. P. Suniantara, “ANALISIS RANDOM FOREST PADA KLASIFIKASI CART UNIVERSITAS TERBUKA Analysis of Random Forest In Inaccuracies CART Classification of Terbuka University Student Graduates,” vol. 13, no. 3, pp. 179–186, 2019.
Z. Firmansyah and N. F. Puspitasari, “Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Berdasarkan Opini Pada Twitter Menggunakan Algoritma Naive Bayes,” J. Tek. Inform., vol. 14, no. 2, pp. 171–178, 2021, [Online]. Available: https://doi.org/10.15408/jti.v14i2.24024.
