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      "keywords": "Analisis Sentimen, Naïve Bayes, Roblox, TF-IDF, SMOTE, Sentiment Analysis.",
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      "abstract": "Penelitian ini bertujuan untuk menganalisis sentimen pengguna Platform X\r\nterhadap game Roblox di Indonesia serta membandingkan kinerja tiga turunan\r\nalgoritma Naive Bayes, yaitu Multinomial Naive Bayes, Bernoulli Naive Bayes,\r\ndan Gaussian Naive Bayes. Data yang digunakan berupa komentar pengguna\r\nPlatform X yang telah melalui proses pra-pemrosesan teks dan diklasifikasikan ke\r\ndalam dua kelas sentimen, yaitu positif dan negatif. Representasi fitur dilakukan\r\nmenggunakan metode TF-IDF, serta diterapkan teknik Synthetic Minority Oversampling\r\nTechnique\r\n(SMOTE)\r\nuntuk mengatasi\r\nketidakseimbangan\r\nkelas.\r\n\r\nHasil\r\n\r\nanalisis sentimen menunjukkan bahwa sentimen positif lebih\r\ndominan dibandingkan sentimen negatif, dengan jumlah 832 data sentimen positif\r\ndan 613 data sentimen negatif. Hal ini mengindikasikan bahwa mayoritas\r\npengguna Platform X di Indonesia memberikan tanggapan yang cenderung positif\r\nterhadap game Roblox.\r\nBerdasarkan hasil evaluasi kinerja model, Multinomial Naive Bayes\r\nmenunjukkan performa terbaik dengan nilai akurasi sebesar 0,885, presisi 0,952,\r\nrecall 0,843, F1-score 0,895, dan AUC sebesar 0,967. Bernoulli Naive Bayes juga\r\nmenunjukkan performa yang kompetitif dengan akurasi 0,882 dan AUC 0,967,\r\nsementara Gaussian Naive Bayes memiliki performa yang relatif lebih rendah\r\ndibandingkan dua model lainnya. Dengan demikian, dapat disimpulkan bahwa\r\nMultinomial Naive Bayes merupakan model yang paling optimal untuk klasifikasi\r\nsentimen pengguna Platform X terhadap game Roblox berbasis representasi fitur\r\nTF-IDF.",
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