Analisis Perbandingan Metode Tf-Idf dan Word2vec pada Klasifikasi Teks Sentimen Masyarakat Terhadap Produk Lokal di Indonesia

Ivan Rifky Hendrawan; Ema Utami; Anggit Dwi Hartanto

doi:10.30591/smartcomp.v11i3.3902

Analisis Perbandingan Metode Tf-Idf dan Word2vec pada Klasifikasi Teks Sentimen Masyarakat Terhadap Produk Lokal di Indonesia

Ivan Rifky Hendrawan, Ema Utami, Anggit Dwi Hartanto

Abstract

Secara umum, proses menghasilkan setiap ulasan produk pada dasarnya terkait dengan tingkat rating, yang membuat pengguna memberikan komentar yang bias.Analisis sentimen dapat diterapkan pada ulasan produk Marketplace sehingga dapat digunakan sebagai saran perbaikan produk untuk penjual dan pesaing sehingga dapat mengetahui produk apa yang disenangi dan dibutuhkan oleh masyarakat.Penelitian ini menggunakan algoritma XGBoost dengan menggunakan dataset bahasa Indonesia yang dikombinasikan dengan TF-IDF dan Word2vec dan akan dievaluasi kombinasi mana yang lebih baik dalam mengklasifikasikan data teks yang tidak seimbang.Berdasarkan penelitian yang telah dilakukan, dua vector space TF-IDF dan Word2vec menghasilkan nilai F1-Score yang berbeda pada algoritma klasifikasi XGBoost, kombinasi Word2vec+XGboost menghasilkan nilai F1-Score lebih tinggi 0.941% dibanding TF IDF+XGBoost 0.940%. Hal ini dikarenakan word2vec lebih baik karena memiliki keunggulan dapat melihat hubungan semantik antar kata.

Kata kunci: word2vec, tfidf, sentimen analisis, XGBoost,

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DOI: https://doi.org/10.30591/smartcomp.v11i3.3902