Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor

DOI:

https://doi.org/10.21111/fij.v6i2.5536

Authors

  • Trifebi Shina Sabrila Universitas Muhammadiyah Malang
  • Veronica Retno Sari Universitas Muhammadiyah Malang
  • Agus Eko Minarno Universitas Muhammadiyah Malang

Abstract

Analisis sentimen merupakan salah satu bidang dari pengolahan data berbentuk teks untuk mengidentifikasi isi yang terkandung dalam teks pada dataset dengan membagi dataset ke dalam dua kelas yaitu sentimen positif dan sentimen negatif. Pada penelitian ini akan dilakukan analisis sentimen terhadap data yang diperoleh dari jejaring sosial Twitter mengenai penanganan Covid-19 oleh pemerintah di Indonesia yang menuai banyak pro dan kontra oleh masyarakat di Indonesia. Tujuan dari penelitian ini adalah untuk mengetahui kecenderungan masyarakat terkait topik tersebut. Metode klasifikasi yang digunakan dalam penelitian ini adalah Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN) dengan ekstraksi fitur Word Embedding. Pengklasifikasian yang dilakukan dengan menggunakan algoritma Support Vector Machine (SVM) dengan menggunakan ekstraksi fitur Word Embedding yaitu Word2Vec menghasilkan akurasi sebesar 85%, presisi 86% , recall 85%, dan nilai AUC sebesar 0.92. Sementara pada algoritma K-Nearst Neighbor (KNN) dengan ekstraksi fitur yang sama, dihasilkan akurasi sebesar 76%, presisi 77%, recall 76% dan nilai AUC sebesar 0.87. Hasil perbandingan dari kedua metode menunjukkan bahwa algoritma Support Vector Machine (SVM) mendapatkan performa yang lebih baik dibandingkan algoritma K-Nearest Neighbor (KNN).

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Submitted

2021-01-22

Accepted

2021-07-25

Published

2024-08-20

Issue

Vol. 6 No. 2 (2021): November

Section

Articles

License

Copyright (c) 2021 Trifebi Shina Sabrila, Veronica Retno Sari, Agus Eko Minarno

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