BERT Sentimen: Fine-Tuning Multibahasa untuk Ulasan Bahasa Indonesia
Penulis
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Khen Dedes
Politeknik Negeri Jember
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Fatimatuzzahra
Politeknik Negeri Jember
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Mas'ud Hermansyah
Politeknik Negeri Jember
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Akas Bagus Setiawan
Politeknik Negeri Jember
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Reza Putra Pradana
Politeknik Negeri Jember
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Annisa Fitri Maghfiroh Harvyanti
Universitas Negeri Jember
DOI:
https://doi.org/10.62712/juktisi.v4i2.585Kata Kunci:
mBERT, Analisis Sentimen, Augmentasi data, Fine-tuning, Precision‑recall trade‑offAbstrak
Penelitian ini mengevaluasi pengaruh teknik augmentasi dan fine‑tuning terhadap kinerja model BERT multibahasa pada tugas klasifikasi sentimen ulasan film berbahasa Indonesia. Dataset awal terdiri dari 1.200 ulasan; 80% digunakan untuk pelatihan dan validasi (n = 960) dan 20% untuk pengujian (n = 240). Data pelatihan diperluas melalui augmentasi menjadi 2.880 sampel sintetis untuk keperluan fine‑tuning. Model kemudian di‑fine‑tune pada korpus yang diperluas dan dievaluasi menggunakan metrik akurasi, precision, recall, dan F1. Pada set pengujian diperoleh akurasi 82,5%, precision untuk kelas positif 76,0%, recall 95,0%, dan F1‑score 84,44%. Matriks kebingungan menunjukkan TP = 114, FN = 6, FP = 36, dan TN = 84, yang mengindikasikan sensitivitas tinggi terhadap ulasan positif namun terdapat proporsi false positive yang relatif besar. Temuan ini mengindikasikan bahwa augmentasi meningkatkan kemampuan model dalam menangkap sinyal positif (tingginya recall), namun memerlukan penyesuaian lebih lanjut untuk mengurangi kesalahan prediksi positif (meningkatkan precision). Secara keseluruhan, hasil penelitian menyediakan bukti bahwa BERT multibahasa mampu menangani tugas sentimen berbahasa Indonesia dengan performa memadai apabila didukung strategi augmentasi dan prosedur validasi yang tepat.Unduhan
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Hak Cipta (c) 2025 Khen Dedes, Fatimatuzzahra, Mas'ud Hermansyah, Akas Bagus Setiawan, Reza Putra Pradana, Annisa Fitri Maghfiroh Harvyanti
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