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      "keywords": "Komunikasi negatif, Naive bayes, Text\r\nmining, Negative communication, Hate speech, Abusive language.",
      "thesis_type": "bachelor",
      "uri": "http:\/\/eprints.amikom.ac.id\/id\/eprint\/32800",
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      "institution": "Universitas AMIKOM Yogyakarta",
      "department": "Fakultas Ilmu Komputer",
      "date": "2026-05-25",
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      "abstract": "Perkembangan media sosial telah mendorong perubahan pola komunikasi\r\nmasyarakat, di mana pengguna dapat menyampaikan pendapat secara bebas dan\r\nterbuka. Namun, kondisi ini juga memicu munculnya komunikasi negatif seperti\r\nhate speech dan abusive language yang dapat menimbulkan konflik sosial,\r\nmenurunkan kualitas diskusi publik, serta berdampak negatif terhadap lingkungan\r\ndigital. Tingginya volume data pada media sosial X (sebelumnya Twitter)\r\nmenyebabkan proses identifikasi komunikasi negatif secara manual menjadi sulit\r\ndilakukan. Oleh karena itu, diperlukan suatu pendekatan otomatis yang mampu\r\nmengidentifikasi dan mengklasifikasikan komunikasi negatif secara efektif dan\r\nefisien.\r\nPenelitian ini menggunakan metode kuantitatif dengan pendekatan text\r\nmining untuk menganalisis komunikasi negatif pada media sosial X. Data yang\r\ndigunakan berupa dataset Indonesian Abusive and Hate Speech Twitter Text yang\r\ntelah dianotasi. Tahapan penelitian meliputi preprocessing data yang terdiri dari\r\ncase folding, tokenizing, stopword removal, dan stemming, kemudian dilanjutkan\r\ndengan proses klasifikasi menggunakan algoritma Naive Bayes. Data dibagi\r\nmenjadi data training dan data testing untuk membangun dan menguji model.\r\nSelanjutnya, dilakukan evaluasi model menggunakan confusion matrix dengan\r\nmetrik akurasi, precision, recall, dan F1-score.\r\nHasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu\r\nmengklasifikasikan komunikasi negatif dengan baik berdasarkan kategori hate\r\nspeech dan abusive language. Penelitian ini memberikan kontribusi dalam\r\npengembangan analisis komunikasi digital berbasis bahasa Indonesia serta dapat\r\ndimanfaatkan oleh pengembang sistem, peneliti, dan pihak terkait dalam\r\nmendeteksi serta mengelola konten negatif di media sosial. Untuk penelitian\r\nselanjutnya, disarankan menggunakan metode yang lebih kompleks atau\r\nmembandingkan beberapa algoritma guna meningkatkan akurasi model.",
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