Deteksi Komentar Cyberbullying Pada Media Sosial Berbahasa Inggris Menggunakan Naïve Bayes Classification

Pardede, Jasman

doi:10.31311/ji.v7i1.6920

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…The method used in this study is Naïve Bayes with the classification results in the form of bully and non-bully comments obtained from the highest probability value. This study resulted in an accuracy value of 80% [8].…”

Section: Introductionmentioning

confidence: 91%

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Analysis of Cyberbullying Level using Support Vector Machine Method

Purnayasa,

Suarjaya,

Dharmaadi

2022

JIM

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Internet users in Indonesia is increasing in every year. The increase caused by several factors, such as the increasingly even distribution of internet infrastructure in Indonesia. The internet has a positive impact such as facilitating communication between individuals, while the negative impact of the internet is intimidation to someone or known as cyberbullying. Cyberbullying has a huge impact on mental health person, causing victim to be angry, depressed, and anxious. This research aims to measure the level of cyberbullying in Indonesia on Twitter using TF-IDF and Support Vector Machine. Classification in this study is classified into two classes, namely cyberbullying and non-cyberbullying. Twitter data used in this study were 3,344,782 tweets that resulted in a cyberbullying classification level of 34.59% and a non-cyberbullying classification level of 65.41%. The best accuracy value obtained is 85%.

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Section: Introductionmentioning

confidence: 91%

“…(5) (6) (7) After getting the equation from the hyperplane, the equation is entered into the decision function sign(f(X)) as in equation (8).…”

Section: Support Vector Machinementioning

confidence: 99%

Analysis of Cyberbullying Level using Support Vector Machine Method

Purnayasa,

Suarjaya,

Dharmaadi

2022

JIM

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“…Contoh media sosial yang banyak digunakan saat ini antara lain Whatsapp, Tiktok, Facebook, Youtube, Twitter, Instagram, dan lain-lain [4]. Media sosial biasa digunakan sebagai sarana komunikasi, informasi serta sebagai media hiburan bagi penggunanya [5].…”

Section: Pendahuluanunclassified

Deteksi Komentar Cyberbullying pada Media Sosial Instagram Menggunakan Algoritma Random Forest

Santoso,

Putri,

Sahbandi

2023

JAMIKA

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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.

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“…Penelitian tentang deteksi cyberbullying telah mulai dilakukan di Indonesia menggunakan variasi metode, bahasa, dan platform media komentar dengan memakai akurasi sebagai tolak ukur pembanding hasil klasifikasi teks [8], [9], [10]. Tetapi penelitian deteksi cyberbullying pada media sosial dengan komentar berbahasa Indonesia (serta sebagian bahasa daerah) pada Youtube belum pernah dilakukan.…”

Section: Pendahuluanunclassified

Deteksi Komentar Cyberbullying Pada YouTube Dengan Metode Convolutional Neural Network – Long Short-Term Memory Network (CNN-LSTM)

Andika,

Kristian,

Setiawan

2023

teknika

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Pada era digital seperti sekarang cyberbullying kerapkali terjadi di berbagai belahan dunia termasuk di Indonesia, hal ini dapat terjadi pada siapa saja dan dimana saja terutama media sosial seperti YouTube melalui fitur komentar semua pengguna yang memiliki akun dapat dengan mudah terlibat cyberbullying hanya melalui berbalas komentar. Penelitian ini bertujuan untuk melakukan deteksi adanya cyberbullying melalui pengumpulan serta pengklasifikasian komentar negatif video pada kanal YouTube dengan konten tertentu berbasis bahasa Indonesia (serta bahasa-bahasa daerah tertentu, seperti Jawa dan Surabaya) melalui metode deep-learning Convolutional Neural Network – Long Short-Term Memory Network (CNN-LSTM). Dataset komentar yang dipakai dalam penelitian dikumpulkan dengan menggunakan Application Program Interface (API) yang telah disediakan oleh Youtube secara gratis dan berbatas kuota secara kumulatif. Terkumpul data komentar total sebanyak 26.918 komentar dengan perincian 9.834 komentar terklasifikasi cyberbullying dan 17.084 komentar terklasifikasi sebagai bukan cyberbullying. Setelah dataset dipakai dalam proses training pada model CNN-LSTM dan menghasilkan sebuah model dengan nilai F1-score sebesar 0,84, model tersebut dipakai dalam sebuah API sederhana yang menerima input beberapa kalimat yang akan dideteksi konten cyberbullying dan menghasilkan output berupa JSON yang berisi hasil klasifikasi dari setiap kalimat yang akan dideteksi.

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Deteksi Komentar Cyberbullying Pada Media Sosial Berbahasa Inggris Menggunakan Naïve Bayes Classification

Cited by 4 publications

References 9 publications

Analysis of Cyberbullying Level using Support Vector Machine Method

Analysis of Cyberbullying Level using Support Vector Machine Method

Deteksi Komentar Cyberbullying pada Media Sosial Instagram Menggunakan Algoritma Random Forest

Deteksi Komentar Cyberbullying Pada YouTube Dengan Metode Convolutional Neural Network – Long Short-Term Memory Network (CNN-LSTM)

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