Hate Speech Detection in Indonesian Language on Instagram Comment Section Using K-Nearest Neighbor Classification Method

Briliani, Annisa; Irawan, Budhi; Setianingsih, Casi

doi:10.1109/iotais47347.2019.8980398

Cited by 26 publications

(13 citation statements)

References 6 publications

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“…Hal itu bisa berdampak cukup besar terhadap peningkatan kasus ujaran kebencian yang terjadi di masyarakat. Prosedur penanganan kasus ujaran kebencian pada saat ini tidak mudah [4]. Sebelum menangani suatu kasus, polisi perlu menentukan apakah suatu kasus dapat digolongkan sebagai kasus ujaran kebencian atau tidak.…”

Section: Pendahuluanunclassified

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Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification

Liang

2021

RESTI

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In social media, it is found that hate speech is conveyed in the form of text, images and videos, as a result it can provoke certain people to do things that are against the law and harm other person. Therefore, it is necessary to make early detection of hate speech by utilizing machine learning algorithms. This study is to analyze the level of accuracy, precision, recall and F1-Score of 3 kinds of algorithms (SVM, XGBoost, and Neural Network) in the classification of hate speech, using datasets sourced from public hate speech on Twitter in Indonesian. The results of the analysis show that the SVM algorithm has a level of accuracy (83.2%), precision (83%), recall (83%) and F1-score (83%), SVM occupies the highest level compared to XGBoost and Neural Network, so the SVM algorithm can be considered for use in hate speech classification

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Section: Pendahuluanunclassified

“…Dimana we ig h t ( w) an d b ia s(b ) ad a lah p a ra m ete r . Fu n g si wei g h t b i sa d ip er lo l eh m ela lu i p er sam a an (4 ).…”

Section: Data Preparationunclassified

Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification

Liang

2021

RESTI

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“…In 2020, Annisa Briliani et al proposed an approach to identify hate speech on Instagram using the knearest neighbor classifier [23]. They collected the data set using Instagram API from Instagram and annotated those data set manually.…”

Section: B Supervised Learning Approachmentioning

confidence: 99%

Automatic Hate Speech Detection: A Literature Review

Mohiyaddeen¹,

Siddiqui²

2021

Int. j. eng. manag. res.

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Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, especially Facebook, and Twitter have given it a global stage where those hate speeches can spread far more rapidly. Every social media platform needs to implement an effective hate speech detection system to remove offensive content in real-time. There are various approaches to identify hate speech, such as Rule-Based, Machine Learning based, deep learning based and Hybrid approach. Since this is a review paper, we explained the valuable works of various authors who have invested their valuable time in studying to identifying hate speech using various approaches.

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“…Dimana 𝑑(𝑋 1 , 𝑋 2 ) sebagai jarak antara variabel X1 dan X2, kemudian X sebagai objek, dan p sebagai jumlah dimensi variabel [14].…”

Section: Case Foldingunclassified

Text Mining Literature Review on Indonesian Social Media

Thenata

2021

JEPIN

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Era sekarang jumlah berita dari berbagai media sosial yang tersebar dalam waktu singkat dan kebutuhan masyarakat untuk mengkonsumsi berita dalam berbagai referensi dapat mempengaruhi kehidupan masyarakat. Hal ini menyebabkan data yang tersebar dapat dikumpulkan dan dimanfaatkan oleh pemerintah, pengusaha, analisis, ataupun peneliti untuk mengidentifikasi tren, mengembangkan bisnis, memprediksi perilaku pelanggan dan lain sebagainya. Pengumpulan data berita dari media sosial tersebut dapat menggunakan text mining yang melibatkan algoritma yakni Naive Bayes, K-NN, dan SVM. Namun, penggunaan algoritma pada studi kasus yang tidak sesuai dapat memberikan hasil yang tidak optimal. Oleh karena itu, penelitian ini akan menganalisis algoritma text mining yang diimplementasikan pada media sosial berbahasa Indonesia dengan memakai metode systematic literature review. Metode ini dimulai dengan melakukan tahap planning yang menetapkan pertanyaan penelitian, kata pencarian, sumber literatur digital, dan standard literatur. Dilanjutkan dengan tahap conducting yang memilih dan mencocokan standard literatur, serta ekstraksi data. Kemudian tahap reporting yang melakukan analisis hasil ekstraksi data sehingga bisa menemumkan informasi dan pengetahuan. Tolak ukur yang menjadi acuan untuk perbandingan yakni pengujian confusion matrix berupa accuracy, precision, dan recall. Adapun hasil dari penelitian ini ditemukan algoritma Naive Bayes memberikan hasil yang stabil tapi kurang optimal jika diterapkan pada studi kasus media sosial berbahasa Indonesia. Sedangkan algortima K-NN dan SVM ditemukan memberikan hasil yang optimal jika diterapkan pada studi kasus media sosial berbahasa Indonesia yang dibuktikan dengan accuracy (50%-98.13%), precision (58.22%-98.48%), dan recall (21.05%-98%).

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Hate Speech Detection in Indonesian Language on Instagram Comment Section Using K-Nearest Neighbor Classification Method

Cited by 26 publications

References 6 publications

Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification

Comparative Analysis of SVM, XGBoost and Neural Network on Hate Speech Classification

Automatic Hate Speech Detection: A Literature Review

Text Mining Literature Review on Indonesian Social Media

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