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      "abstract": "Isu mafia BBM memicu beragam respon masyarakat yang terekam pada\r\nkolom komentar TikTok. Untuk melihat kecenderungan opini publik secara lebih\r\nterstruktur, dilakukan analisis sentimen menggunakan model IndoBERT. Data\r\nkomentar yang bersifat teks bebas diproses melalui pembersihan teks, penyamaan\r\nformat penulisan, normalisasi kata, penghapusan kata umum yang kurang\r\nmembantu, serta penghapusan data kosong dan duplikat agar data lebih konsisten.\r\nPelabelan sentimen dilakukan secara otomatis menggunakan leksikon InSet\r\nsehingga terbentuk tiga kelas sentimen negatif, netral, dan positif. Model\r\nIndoBERT dilatih menggunakan pembagian data latih, validasi, dan uji dengan\r\nproporsi kelas yang dijaga, serta dipilih konfigurasi terbaik dengan mencoba\r\nbeberapa setelan pelatihan yang berbeda. Evaluasi pada data uji menghasilkan\r\nakurasi 0,8786 dengan F1 score macro 0,8785 dan F1 score weighted 0,8793.\r\nSecara per kelas, performa terbaik terdapat pada sentimen negatif dengan F1 score\r\n0,90, disusul positif 0,88 dan netral 0,86. Kesalahan klasifikasi paling banyak\r\nterjadi pada kelas netral, yang menunjukkan bahwa komentar netral sering memiliki\r\nciri yang mirip dengan komentar negatif atau positif. Hasil ini menunjukkan\r\nIndoBERT cukup efektif untuk klasifikasi sentimen komentar TikTok terkait isu\r\nmafia BBM dan dapat digunakan sebagai dasar pemetaan kecenderungan opini\r\npublik.",
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        "000.000.004"
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      "institution": "Universitas AMIKOM Yogyakarta",
      "keywords": "Analisis sentimen, Tiktok, IndoBERT, Inset, Mafia BBM, Sentiment Anlyst, Fuel mafia."
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