FT ANALISIS PERBANDINGAN NAIVE BAYES DAN SVM DALAM KLASIFIKASI GENDER DI PLATFORM TWITTER
Abstract
Munculnya media sosial telah menghasilkan sejumlah besar data tekstual yang rentan terhadap analisis untuk memahami perilaku pengguna. Penelitian ini melakukan analisis komparatif dari dua algoritma klasifikasi teks, Naive Bayes dan Support Vector Machine (SVM), dengan aplikasi untuk menentukan gender dari komentar pengguna di platform media sosial X (sebelumnya Twitter). Kumpulan data diperoleh dengan metode web scraping dan kemudian dipre-processing menggunakan mekanisme cleaning teks dan pengubahan singkatan bersama dengan ekstraksi fitur berbasis TF-IDF. Kuantitas data yang berbeda (1000, 3000, 5000) dan rasio pembagian data (70:30 dan 80:20) digunakan untuk mengevaluasi kedua algoritma. Hasilnya menunjukkan bahwa SVM mengungguli Naive Bayes dalam hal semua matriks pada dataset yang lebih besar, sementara Naive Bayes terus mempertahankan kualitasnya pada dataset yang lebih kecil. Temuan ini memiliki implikasi yang signifikan untuk pemilihan metode klasifikasi yang sesuai untuk analisis data komentar media sosial.
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