{
      "title": "KOMPARASI MODEL KLASIFIKASI NAÏVE BAYES DAN\r\nSVM DALAM ANALISIS SENTIMEN RESPON PUBLIK PADA\r\nMOMEN 100 HARI KERJA PRABOWO DI PLATFORM X",
      "divisions": [
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      "subjects": [
        "000.000.000A"
      ],
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        {
          "name": {
            "given": "Alfonsus Ferdy Mahardeka",
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          "nim": "22.12.2310"
        }
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      "institution": "Universitas AMIKOM Yogyakarta",
      "status_changed": "2026-07-08 04:13:44",
      "keywords": "Analisis Sentimen, Naïve Bayes, Support Vector Machine, TF-IDF,\r\nSMOTE, Sentiment Analysis.",
      "thesis_name": "skripsi",
      "thesis_type": "bachelor",
      "date": "2026-02-09",
      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/32004",
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      "abstract": "Perkembangan teknologi informasi telah mendorong media sosial menjadi\r\nruang interaksi publik yang penting dalam menyampaikan opini dan tanggapan\r\nterhadap kebijakan pemerintah. Pada momen 100 hari kerja pemerintahan Prabowo\r\nSubianto, berbagai tanggapan masyarakat yang muncul di platform X (Twitter)\r\nmembentuk kumpulan data tekstual yang mencerminkan kecenderungan sentimen\r\npublik. Kondisi tersebut menjadikan platform X sebagai sumber data yang relevan\r\nuntuk dianalisis guna memahami persepsi masyarakat secara lebih luas dan objektif.\r\nPenelitian ini bertujuan untuk mengidentifikasi kecenderungan sentimen publik\r\nserta membandingkan kinerja dua algoritma klasifikasi teks, yaitu Naïve Bayes dan\r\nSupport Vector Machine (SVM), dalam konteks data sosial-politik berbahasa\r\nIndonesia.\r\nData penelitian terdiri dari 1.369 tweet yang dikumpulkan melalui proses\r\nscraping. Data tersebut kemudian melalui tahapan pra-pemrosesan teks, pelabelan\r\nmanual, pembobotan Term Frequency–Inverse Document Frequency (TF-IDF),\r\nserta penyeimbangan data menggunakan Synthetic Minority Oversampling\r\nTechnique (SMOTE) yang diterapkan pada data latih. Model klasifikasi dilatih dan\r\ndievaluasi menggunakan metrik accuracy, precision, recall, dan F1-score, serta\r\ndivisualisasikan melalui confusion matrix. Selain itu, analisis learning curve\r\ndilakukan untuk melihat pola pembelajaran masing-masing model.\r\nHasil penelitian menunjukkan bahwa sentimen publik terhadap 100 hari\r\nkerja pemerintahan Prabowo Subianto cederung didominnasi oleh sentimen positif\r\ndan netral. Dari sisi performa model, SVM Linear memperoleh accuracy sebesar\r\n0,781 dan F1-score sebesar 0,78, lebih tinggi dibandingkan Naïve Bayes dengan\r\naccuracy sebesar 0,766 dan F1-score sebesar 0,76. Berdasarkan hasil tersebut, SVM\r\nLinear dapat disimpulkan sebagai model yang lebih optimal dalam analisis\r\nsentimen publik pada konteks sosial-politik berbahasa Indonesia. Temuan ini\r\ndiharapkan dapat memberikan kontribusi teoretis dalam pengembangan analisis\r\nsentimen serta manfaat praktis bagi pemerintah, lembaga survei, dan pengembang\r\nsistem informasi.",
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