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      "keywords": "Analisis Sentimen, Support Vector Machine, TF-IDF, Twitter, DPR\r\nRI, Sentiment Analysis",
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            "given": "Aditya Farrel"
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      "date": "2026-02-03",
      "type": "thesis",
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      "title": "ANALISIS SENTIMEN TERHADAP ISU BUBARKAN DPR DI\r\nPLATFORM X MENGGUNAKAN PENDEKATAN SUPPORT\r\nVECTOR CLASSIFIER",
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      "abstract": "Perkembangan media sosial telah mengubah pola komunikasi masyarakat\r\ndalam mengekspresikan opini terkait isu sosial dan politik. Twitter (X) sebagai\r\nplatform real-time menjadi ruang yang mencerminkan sentimen publik secara\r\nspontan, termasuk pada isu viral “Bubarkan DPR” yang mencuat sebagai bentuk\r\nketidakpuasan terhadap kinerja legislatif. Penelitian ini bertujuan menganalisis\r\nkecenderungan sentimen publik terhadap isu tersebut menggunakan Support Vector\r\nClassifier. Data yang digunakan berjumlah 2.165 cuitan yang melalui proses\r\npelabelan, prapemrosesan, dan klasifikasi sentimen positif serta negatif. Hasil\r\nevaluasi menunjukkan bahwa Support Vector Classifier memiliki performa sangat\r\nbaik dengan akurasi 84% dan weighted F1-Score 81% (presisi 83%, recall 84%).\r\nModel secara konsisten menghasilkan performa tertinggi pada sentimen negatif\r\n(F1-Score 90%) yang juga menjadi kelompok dominan dalam dataset. Temuan ini\r\nmenunjukkan bahwa Support Vector Classifier efektif untuk analisis teks informal\r\nberbahasa Indonesia dan mengungkap bahwa persepsi publik terhadap DPR dalam\r\nisu ini cenderung negatif.",
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