Analisis Sentimen Terhadap Kemajuan Kecerdasan Buatan di Indonesia Menggunakan BERT dan RoBERTa
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Abstract
Kemajuan dalam bidang kecerdasaan buatan (AI) dalam beberapa tahun belakang menjadi sesuatu yang sering dibahas dikalangan masyarakat di Indonesia. Berbagai prespektif muncul hampir disemua platfom sosial media untuk menyampaikan tanggapan terhadap suatu topik. Pada penelitian yang dilakukan memanfaatkan analisis sentimen sehingga dapat mengintepretasi sentimen pada masyarakat dengan manfaatkan komentar masyarakat di kolom komentar YouTube sebagai objek penelitian dikarekan relevansi dengan topik sebesar 60-80%. Selain itu, peneliti menggunakan fine-tuning BERT dan RoBERTa sebagai labeling sentimen negatif, netral ataupun positif pada setiap komentar yang telah dikumpulkan menggunakan Python serta library Beautifulsoup dan selenium. Dataset yang dikumpulkan sebanyak 5796. Namun, setelah dilakukan preprosesing dataset menjadi 5788 yang terbagi menjadi 3028, 1435, 1333 dari sentimen negatif, positif, dan netral untuk labeling menggunakan RoBERTa. Sedangkan, BERT yaitu 3106, 1422, 1268 dari sentimen negatif, positif, dan netral. Pada penelitian ini juga mendapatkan hasil bahwa model indobenchmark/indobert-base-p1 dapat digunakan untuk labeling dataset berbahasa Indonesia. Selain itu, training model dengan skema 1 persebaran dataset 70:30:30 dan skema 2 adalah 80:20:20, menjadi model yang memiliki kinerja sebesar 84% untuk akurasi validation dan 83% akurasi testing pada skema 1. 83% akurasi validasi 84% akurasi tesing pada skema 2. Selanjutnya, peneliti menyarankan untuk adanya keterlibatan stackholder terkait penggunaan AI di Indonesia.
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