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      "title": "PERBANDINGAN KINERJA ALGORITMA NAIVE BAYES\r\nDAN SUPPORT VECTOR MACHINE (SVM) TERHADAP\r\nSENTIMEN TWITTER PADA PROGRAM MAKAN \r\nBERGIZI GRATIS",
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      "date": "2026-02-20",
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      "abstract": "Program Makan Bergizi Gratis merupakan kebijakan pemerintah yang bertujuan\r\nmeningkatkan kualitas gizi masyarakat, khususnya anak-anak dan pelajar. Namun,\r\nimplementasi kebijakan ini menimbulkan beragam respons di masyarakat yang\r\nterekspresikan secara luas melalui media sosial Twitter (X). Perbedaan pandangan,\r\nkritik, serta dukungan terhadap program tersebut dapat memengaruhi persepsi\r\npublik dan kepercayaan masyarakat terhadap kebijakan pemerintah. Oleh karena\r\nitu, diperlukan analisis sentimen untuk memahami kecenderungan opini\r\nmasyarakat serta dampaknya terhadap lingkungan sosial dan pengambilan\r\nkeputusan kebijakan publik.\r\n\r\nPenelitian ini menggunakan data opini masyarakat yang dikumpulkan dari media\r\nsosial Twitter (X) terkait program Makan Bergizi Gratis. Tahapan penelitian\r\nmeliputi pengumpulan data, preprocessing teks, pelabelan data secara otomatis\r\nmenggunakan pendekatan berbasis kamus lexicon, serta pembentukan fitur dengan\r\nmetode Term Frequency–Inverse Document Frequency (TF-IDF). Dataset\r\nkemudian dibagi menjadi data latih dan data uji dengan perbandingan 80:20. Proses\r\nklasifikasi sentimen dilakukan menggunakan dua algoritma machine learning, yaitu\r\nNaive Bayes dan Support Vector Machine (SVM). Evaluasi kinerja model\r\ndilakukan menggunakan confusion matrix dan metrik evaluasi berupa accuracy,\r\nprecision, recall, dan F1-score.\r\n\r\nHasil penelitian menunjukkan bahwa algoritma SVM memiliki kinerja yang lebih\r\nbaik dibandingkan Naive Bayes dalam mengklasifikasikan sentimen masyarakat\r\nterhadap program Makan Bergizi Gratis. Model SVM memperoleh tingkat akurasi\r\nyang lebih tinggi, sehingga dinilai lebih efektif dalam menangani data teks berbasis\r\nTF-IDF. Hasil penelitian ini dapat dimanfaatkan oleh peneliti, akademisi, serta\r\npemerintah sebagai bahan evaluasi kebijakan publik. Penelitian selanjutnya\r\ndisarankan untuk menggunakan dataset yang lebih besar dan metode pelabelan\r\nberbasis pembelajaran mendalam guna meningkatkan akurasi analisis sentimen.",
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