Klasifikasi Ujaran Kebencian pada Cuitan dalam Bahasa Indonesia
DOI:
https://doi.org/10.24002/jbi.v10i2.2451Abstract
Abstract.
The sheer amount of hate speech in social media is making people nauseous. The amount of hate speech these days keeps increasing and yet, there was no preventive act to counter back the hate speech. Pre-existing hate speech detection is also not yet available in Bahasa Indonesia. A machine learning model that is able to recognize hate speech in Bahasa Indonesia will be explained in this article. The model will compare pre-existing methods in machine learning. Naive Bayes, SVM, and Logistics Regression are the methods that will be used for the model. Some of the parameters in the test will be altered to achieve the maximum value for detecting hate speech. The expectation is a machine learning model that is able to recognize hate speech in Bahasa Indonesia accurately. Expected accuracy is above 85%. After the experiment, the highest accuracy achieved was at 98%, while the lowest accuracy was only 80%.
Keywords: Hate speech detection, machine learning model, social media, Bahasa Indonesia, tweets
Abstrak.
Banyaknya ujaran kebencian yang ada di media sosial sudah membuat jengah. Ujaran kebencian tersebut makin marak dijumpai namun masih belum ada upaya preventif dari media sosial untuk menangkalnya. Deteksi ujaran kebencian yang sudah dibuat juga belum tersedia dalam Bahasa Indonesia. Sebuah model pembelajaran mesin yang dapat mengenali ujaran kebencian dengan Bahasa Indonesia akan dibahas pada naskah ini. Dalam model tersebut dibandingkan beberapa metode pembelajaran mesin yang ada. Metode yang digunakan dalam pengujian adalah Naïve Bayes, SVM, dan Logistic Regression. Dalam pengujian, beberapa parameter akan diubah-ubah sehingga didapatkan nilai paling maksimal dalam deteksi ujaran kebencian. Hasil yang diharapkan adalah sebuah model pembelajaran mesin. Model tersebut diharapkan dapat mengenali ujaran kebencian berbahasa Indonesia secara akurat. Akurasi yang diharapkan adalah diatas 85%. Setelah percobaan, didapatkan nilai akurasi paling tinggi yaitu 98%, sedangkan nilai akurasi paling rendah yaitu 80%.
Kata Kunci: Deteksi ujaran kebencian, model pembelajaran mesin, media sosial, Bahasa Indonesia, cuitan
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