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<note>Metode Semantic Naive Bayes dalam mengklasifikasikan komentar teks pada gambar di media sosial, dengan fokus pada komentar dalam bentuk gambar. Media sosial sering dipenuhi komentar negatif yang berdampak buruk pada kesehatan mental individu. Penelitian sebelumnya menunjukkan bahwa metode Semantic Naive Bayes, yang menggunakan representasi semantik teks, lebih unggul dalam analisis sentimen dibandingkan Naive Bayes tradisional. Metode ini diaplikasikan dalam proses yang mencakup OCR, preprocessing teks, dan klasifikasi. Hasil penelitian menunjukkan bahwa Semantic Naive Bayes mencapai akurasi 97%, precision 95%, recall 95%, dan f1-Score 95%, sementara Naive Bayes tradisional hanya mencapai akurasi 59%, precision 66%, recall 66%, dan f1-Score 64%. Temuan ini mengonfirmasi bahwa Semantic Naive Bayes menawarkan peningkatan signifikan dalam identifikasi komentar negatif, memberikan kontribusi penting dalam analisis sentimen media sosial. Kata kunci: Sentimen analisis, Semantic, naïve bayes, akurasi.</note>
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