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      "abstract": "Penelitian ini dilatarbelakangi oleh banyaknya data teks yang dihasilkan \r\npengguna media sosial X, sementara informasi demografis seperti jenis kelamin\r\ntidak selalu ditampilkan secara terbuka. Kondisi ini menyebabkan identifikasi\r\nkarakteristik pengguna menjadi sulit dilakukan secara langsung. Oleh karena itu,\r\npenelitian ini bertujuan untuk mengklasifikasikan jenis kelamin pengguna\r\nberdasarkan teks cuitan menggunakan pendekatan Natural Language Processing\r\ndan machine learning. Dataset diperoleh dari Kaggle dengan jumlah awal sekitar\r\n20.000 data, kemudian dilakukan penyaringan dan pelabelan ulang hingga\r\ndiperoleh 1.026 data yang terdiri dari 456 data perempuan dan 570 data laki-laki.\r\nTahap penelitian meliputi pra-pemrosesan teks, ekstraksi fitur menggunakan TF-\r\nIDF, serta pemodelan menggunakan algoritma Logistic Regression, Random\r\nForest, dan Support Vector Machine. Evaluasi dilakukan menggunakan K-Fold\r\nCross Validation dengan metrik accuracy, precision, recall, dan F1-score. Hasil\r\npenelitian menunjukkan adanya perbedaan pola bahasa antara pengguna laki-laki\r\ndan perempuan yang dapat dimanfaatkan dalam proses klasifikasi. Algoritma\r\nSupport Vector Machine menghasilkan performa terbaik dengan akurasi rata-rata\r\n89,76%. Penelitian ini diharapkan dapat dimanfaatkan dalam pengembangan sistem\r\nanalisis pengguna media sosial, pemasaran digital, serta penelitian lanjutan di\r\nbidang analisis teks.",
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      "title": "LASIFIKASI JENIS KELAMIN PENGGUNA X \r\nBERDASARKAN CUITAN MENGGUNAKAN ALGORITMA\r\nLOGISTIC REGRESSION, RANDOM FOREST, DAN\r\nSUPPORT VECTOR MACHINE",
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