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      "abstract": "Pelecehan seksual secara verbal merupakan salah satu bentuk kekerasan\r\nyang banyak ditemukan di media sosial, termasuk pada kolom komentar\r\npengguna di platform Tik Tok. Intensitas komentar yang tinggi dengan\r\nkarakteristik komentar yang singkat, tidak baku, serta sering disampaikan secara\r\nimplisit menyebabkan komentar sulit terdeteksi. Kondisi ini berpotensi\r\nmenyebabkan lingkungan digital yang tidak aman khususnya yang menjadi objek\r\nseksualisasi. Penelitian ini bertujuan untuk membangun model klasifikasi yang\r\nmampu mendeteksi komentar bermuatan pelecehan seksual verbal dengan\r\nmembandingkan dua algoritma klasifikasi, yaitu K-Nearest Neighbor (KNN) dan\r\nSupport Vector Machine (SVM). Data penelitian berjumlah 7.063 yang didapat\r\ndari komentar konten publik figur Korea di Tik Tok, kemudian diberi label secara\r\nmanual ke dalam kelas pelecehan seksual dan non-pelecehan seksual. Data\r\nkomentar diproses melalui tahap pembersihan teks, transformasi data teks ke\r\ndalam bentuk vektor numerik menggunakan word embedding LaBSE\r\n(Language-agnostic BERT Sentence Embedding), serta pembagian data. Kedua\r\nalgoritma dilatih dengan data yang sama dan dievaluasi menggunakan metrik\r\naccuracy, precision, recall, dan F1-score untuk mengetahui performa model\r\ndalam mendeteksi komentar pelecehan seksual verbal. Hasil penelitian\r\nmenunjukkan algoritma SVM dengan skenario pembagian data 80:20 memberikan\r\nperforma lebih baik dan stabil dibandingkan KNN. Nilai akurasi mencapai 84%\r\ndengan nilai recall dan F1-score kelas pelecehan seksual lebih tinggi dibanding\r\nKNN, menunjukkan kemampuan SVM lebih baik dalam mendeteksi kelas\r\nminoritas. Secara keseluruhan, penelitian menunjukkan kombinasi algoritma\r\nSVM dan LaBSE lebih efektif dalam mendeteksi komentar pelecehan seksual\r\nverbal berbasis teks dan dapat dimanfaatkan sebagai dasar pengembangan sistem\r\nmoderasi konten platform media sosial guna menciptakan ruang digital yang lebih\r\naman.",
      "type": "thesis",
      "thesis_name": "skripsi",
      "thesis_type": "bachelor",
      "keywords": "Support Vector Machine, K-Nearest Neighbor, LaBSE, Pelecehan \r\nSeksual Verbal, Machine Learning, Verbal Sexual\r\nHarassment.",
      "status_changed": "2026-07-08 03:20:02"
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