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      "title": "ANALISIS SENTIMEN PUBLIK TERHADAP PROGRAM\r\nMAKANAN BERGIZI GRATIS MENGGUNAKAN MODEL\r\nINDOBERT",
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      "abstract": "Program Makanan Bergizi Gratis (MBG) merupakan kebijakan pemerintah \r\nyang bertujuan meningkatkan kualitas gizi dan sumber daya manusia di Indonesia.\r\nKebijakan ini memicu berbagai respons publik di media sosial X, baik berupa\r\ndukungan, kritik, maupun opini netral. Volume opini yang besar dan tidak\r\nterstruktur menyulitkan analisis secara manual, sehingga diperlukan pendekatan\r\nkomputasional berbasis Natural Language Processing (NLP). Penelitian ini\r\nbertujuan untuk menganalisis sentimen publik terhadap Program MBG\r\nmenggunakan model IndoBERT.\r\nMetode penelitian diawali dengan pengumpulan data melalui web scraping\r\nmenggunakan Tweet Harvest pada periode Agustus hingga November 2025.\r\nDataset awal sebanyak 4.874 tweet diproses melalui tahapan Exploratory Data\r\nAnalysis (EDA), preprocessing (case folding, cleaning, dan normalisasi), serta \r\npelabelan otomatis menggunakan pendekatan lexicon-based. Data kemudian\r\ndibagi dengan rasio 80% untuk pelatihan dan 20% untuk pengujian menggunakan\r\nmetode stratified split. Model IndoBERT-Large-P2 diinisialisasi dan dilakukan\r\nproses fine-tuning untuk klasifikasi tiga kelas sentimen, yaitu positif, netral, dan\r\nnegatif.\r\nHasil penelitian menunjukkan bahwa \r\nIndoBERT mampu \r\nmengklasifikasikan sentimen publik secara efektif berdasarkan metrik accuracy,\r\nprecision, recall, dan macro F1-score. Confusion matrix menunjukkan bahwa\r\nkesalahan klasifikasi paling sering terjadi pada kelas netral. Penelitian ini\r\nmemberikan kontribusi dalam pemetaan opini publik berbasis NLP serta dapat\r\ndimanfaatkan oleh pemerintah dan pemangku kebijakan untuk memahami respons\r\nmasyarakat secara kuantitatif.",
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      "department": "Fakultas Ilmu Komputer"
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