Klasifikasi Hate Speech dan Emosi Dalam Teks Berbahasa Indonesia Pada Pengguna Twitter Menggunakan Metode Naïve Bayes Classifier

Chandra Hary Pratama; Yulian Findawati

doi:10.47134/ijat.v1i3.3105

Authors

Chandra Hary Pratama Universitas Muhammadiyah Sidoarjo
Yulian Findawati Universitas Muhammadiyah Sidoarjo

DOI:

https://doi.org/10.47134/ijat.v1i3.3105

Keywords:

Klasifikasi, Ujaran Kebencian, Emosi, Naïve Bayes, Twitter

Abstract

Ujaran kebencian merupakan salah satu bentuk ekspresi yang menghasut, menyebarkan, membenarkan, atau mendorong kebencian, diksriminasi serta kekerasan atas individu dan kelompok sebab berbagai alasan. Hate speech biasanya ditemukan pada sosial media yang terhubung dengan internet, salah satunya pada penelitian ini melalui sosial media twitter dengan menggunakan metode Naïve Bayes Classifier. Dataset yang digunakan pada penelitian ini berjumlah 1800 data berlabel bukan ujaran kebencian dan 2250 data berlabel ujaran kebencian dengan perbandinghan 60% data latih dan 40% data uji. Hasil evaluasi data uji dengan confusion matrix diperoleh pengukuran matrix mean accuracy for hate speech classification 0,89 dan matrix mean accuracy for emotion classification 0,59. Berdasarkan hasil yang didapat tersebut dapat diambil kesimpulan bahwa untuk melakukan klasifikasi hate speech dan emosi pada Twitter menggunakan Naïve Bayes hasil paling bagus dengan Confusion Matrix tanpa melakukan seleksi fitur Information Gain.

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