File Tugas Akhir Periode 1 2025

File Tugas Akhir Periode 1 2025

• By Dama, Desi Masdin;
• Tugas Akhir
• 38

Setiap harinya, terdapat ribuan artikel berita dipublikasikan oleh berbagai portal berita nasional, yang masing-masing memiliki gaya bahasa, pendekatan jurnalistik, serta framing (pembingkaian informasi) yang beragam. Pendekatan manual yang dilakukan untuk membaca dan mengkaji ribuan berita tentu memerlukan waktu, tenaga dan sumber daya yang besar. Realitas tersebut menimbulkan kebutuhan terhadap pendekatan otomatis yang mampu mengidentifikasi dan mengklasifikasikan sentimen secara cepat dan akurat, yang tidak hanya berguna untuk mendeteksi kecenderungan opini publik, tetapi juga membantu dalam mengidentifikasi potensi bias atau kecenderungan opini publik terhadap isu tertentu. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksperimen untuk menguji secara sistematis performa model klasifikasi sentimen pada berita digital. Dengan memanfaatkan teknologi kecerdasan buatan, khususnya Natural Language Processing (NLP), penelitian ini menerapkan model IndoBERTa yang telah melalui proses fine-tuning untuk melakukan klasifikasi sentimen pada berita digital dengan mengacu pada kerangka kerja CRISP-DM yang terdiri dari enam tahapan. Berdasarkan hasil evaluasi, model menunjukkan performa tinggi dengan Accuracy dan Cohen’s Kappa sebesar 98%, yang berarti bahwa distribusi dari klasifikasi sentimen menunjukkan bahwa model mampu menangkap konteks topik berita. Berdasarkan hal tersebut, untuk mendukung penerapannya, model ini diintegrasikan ke dalam pipeline berbasis Streamlit yang mengotomatisasi proses mulai dari pengumpulan berita, klasifikasi sentimen, hingga visualisasi dan ekspor hasil analisis.

Kata Kunci: Berita Digital, Analisis Sentimen, NLP, IndoBERTa, Fine-Tuning

• Katakunci Berita Digital, Analisis Sentimen, NLP, IndoBERTa, Fine-Tuning
• URL https://drive.google.com/drive/folders/1yF54no_iwk1UVuzgjDjy2R00t_q4Libf?usp=sharing
• Penerbit ,

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