Perbandingan Kinerja IndoBERT dan IndoRoBERTa dengan Penerapan SMOTE dalam Deteksi Ujaran Kebencian Berbahasa Indonesia

Muhammad Mutawakkil  Alallah; Indra  Rosyidah

doi:10.62048/qjms.v3i2.167

Penulis

Muhammad Mutawakkil Alallah Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia
Indra Rosyidah Universitas Nurul Jadid, Probolinggo, Indonesia

DOI:

https://doi.org/10.62048/qjms.v3i2.167

Kata Kunci:

ujaran kebencian, pemrosesan bahasa alami, transformer, IndoBERT, IndoRoBERTa

Abstrak

Perkembangan media sosial di Indonesia meningkatkan interaksi digital sekaligus memunculkan masalah ujaran kebencian yang berdampak pada kualitas komunikasi dan stabilitas sosial. Penelitian ini bertujuan membandingkan performa dua model Transformer, IndoBERT dan IndoRoBERTa, dalam klasifikasi ujaran kebencian berbahasa Indonesia serta mengevaluasi pengaruh teknik data balancing SMOTE. Dataset berupa data Twitter berbahasa Indonesia yang telah melalui tahap pre-processing dan dibagi menggunakan stratified train-test split 80:20. Pelatihan model dilakukan melalui fine-tuning, sedangkan evaluasi menggunakan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa IndoRoBERTa lebih unggul dibandingkan IndoBERT pada seluruh metrik serta mampu mengurangi kesalahan klasifikasi. Penerapan SMOTE juga meningkatkan kemampuan model dalam mendeteksi kelas minoritas, terutama pada recall. Temuan ini menunjukkan bahwa kombinasi model Transformer dan teknik data balancing efektif meningkatkan akurasi serta keseimbangan klasifikasi ujaran kebencian. Temuan ini mengindikasikan bahwa kombinasi IndoRoBERTa dan SMOTE berpotensi mendukung pengembangan sistem moderasi konten otomatis yang lebih akurat dan adaptif pada media sosial berbahasa Indonesia.

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