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      "title": "PERBANDINGAN ALGORITMA NAIVE BAYES DAN SVM\r\nDALAM ANALISIS SENTIMEN TWITTER TENTANG\r\nKENAIKAN PPN 12%",
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      "abstract": "Penelitian ini bertujuan untuk menganalisis sentimen masyarakat di Twitter\r\nterkait kebijakan kenaikan Pajak Pertambahan Nilai (PPN) sebesar 12% serta\r\nmembandingkan kinerja algoritma Naive Bayes dan Support Vector Machine\r\n(SVM) dalam klasifikasi sentimen. Data dikumpulkan dari Twitter dan melalui\r\ntahapan preprocessing yang meliputi pembersihan teks, case folding, tokenization,\r\nstopword removal, dan stemming. Data kemudian dibagi menjadi data latih dan data\r\nuji untuk proses evaluasi model. Hasil eksperimen menunjukkan bahwa model\r\nSVM yang dioptimasi menggunakan kernel Radial Basis Function (RBF)\r\nmemperoleh kinerja terbaik dengan tingkat akurasi sebesar 79,93%. Berdasarkan\r\nclassification report, SVM menunjukkan performa lebih konsisten dalam\r\nmengidentifikasi kelas Negative (f1-score = 0,86) dibandingkan kelas Positive (f1score\r\n=\r\n0,65),\r\ndengan\r\nnilai\r\nweighted\r\naverage\r\nf1-score\r\nsebesar\r\n0,80.\r\nSementara\r\nitu,\r\n\r\nmodel\r\nNaive\r\nBayes\r\nmemperoleh\r\nakurasi\r\nsebesar\r\n74,07%,\r\ndengan\r\nf1-score\r\nsebesar\r\n\r\n0,83\r\npada\r\nkelas\r\nNegative\r\ndan\r\n0,46\r\npada\r\nkelas\r\nPositive,\r\nserta\r\nweighted\r\naverage\r\nf1score\r\n\r\nsebesar 0,71. Berdasarkan hasil tersebut, dapat disimpulkan bahwa SVM\r\nmemiliki performa yang lebih unggul dibandingkan Naive Bayes dalam analisis\r\nsentimen Twitter terkait kebijakan kenaikan PPN 12%.",
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