Analisis Sentimen Pengguna X terhadap IKN Menggunakan Word2Vec dan IndoBERT
DOI:
https://doi.org/10.30865/json.v7i3.9468Keywords:
Analisis sentimen, IKN, Deep Learning; , Word2Vec, IndoBERTAbstract
Pembangunan Ibu Kota Nusantara (IKN) di Kalimantan Timur adalah kebijakan strategis nasional yang menimbulkan beragam respons di tengah masyarakat. Media sosial X (Twitter) salah satu ruang utama bagi publik dengan tujuan menyampaikan pandangan, baik berupa dukungan ataupun kritik, terhadap kebijakan tersebut. Fokus penelitian ini adalah untuk mengevaluasi persepsi masyarakat tentang pembangunan IKN dengan membandingkan kinerja algoritma Word2Vec yang diklasifikasikan menggunakan Support Vector Machine (SVM) dan model IndoBERT Fine-Tuning. Data penelitian diperoleh dengan proses crawling tweet berbahasa Indonesia pada periode 2022 hingga 2025 dengan memanfaatkan istilah yang relevan terhadap pembangunan IKN. Sebanyak 2.667 dataset yang telah menjalani tahap pelabelan manual dan otomatis serta preprocessing, yang terdiri dari text cleaning, case folding, normalisasi, dan tokenisasi, digunakan dalam proses pemodelan. Dataset kemudian dibagi menjadi data latih dan data uji dengan rasio 80:20. Evaluasi performa model dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model IndoBERT Fine-Tuning dengan pelabelan manual menghasilkan performa terbaik dengan nilai accuracy sebesar 0.900 dan F1-score sebesar 0.899 serta mampu mengungguli pendekatan Word2Vec–SVM. Analisis ini menegaskan bahwa model berbasis Transformer lebih efektif dalam memahami konteks bahasa Indonesia pada data media sosial. Hasil analisis sentimen selanjutnya disajikan dalam bentuk dashboard interaktif untuk memudahkan interpretasi opini publik terhadap pembangunan Ibu Kota Nusantara.
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