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      "date": "2026-02-10",
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      "abstract": "Teknologi dan akses kesehatan yang maju belum sepenuhnya mampu\r\nmenghentikan penyebaran virus influenza yang masih menjadi ancaman kesehatan\r\nmasyarakat di Indonesia akibat penyebarannya yang cepat. Meskipun vaksinasi\r\nmerupakan tindakan pencegahan yang efektif, tingkat penerimaannya masih\r\nrendah karena faktor-faktor seperti kurangnya pemahaman, karakteristik individu,\r\nbudaya, dan keterbatasan akses, yang mengakibatkan perlindungan kesehatan di\r\nmasyarakat tidak merata. Penelitian ini bertujuan untuk menganalisis dan\r\nmembandingkan kinerja algoritma Support Vector Machine (SVM), Naïve Bayes,\r\ndan Random Forest dalam mengklasifikasikan penerimaan vaksin influenza\r\ndengan menggunakan metodologi CRISP-DM (Cross-Industry Standard Process\r\nfor Data Mining), yang mencakup tahapan dari business understanding hingga\r\nevaluation. Dengan menggunakan data sekunder dari Kaggle.com, hasil penelitian\r\nmenunjukkan bahwa Random Forest mencapai kinerja terbaik dengan akurasi\r\nsebesar 85%, disusul oleh SVM sebesar 83% dan Naïve Bayes sebesar 70%. Hal\r\nini membuktikan keunggulan Random Forest dalam menangani data yang\r\nkompleks dan tidak seimbang, dan temuan ini diharapkan dapat menjadi referensi\r\nilmiah untuk pengembangan data yang lebih luas serta membantu proses\r\npengambilan keputusan dan edukasi di bidang kesehatan masyarakat.",
      "thesis_name": "skripsi",
      "thesis_type": "bachelor",
      "keywords": "Klasifikasi, Vaksin Influenza, Support Vector Machine, Naïve\r\nBayes, Random Forest, Classification, Influenza Vaccine.",
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