Pemilihan Fitur Chi Square Pada Algoritma Naïve Bayes dan Pengaruhnya Terhadap Analisis Sentimen Masyarakat Indonesia Tentang Pembelajaran Tatap Muka Pada Masa Pandemi Covid-19
DOI:
https://doi.org/10.23887/jstundiksha.v12i1.51899Keywords:
Sentiment Analysis, Chi Square, Covid-19, Naïve Bayes, Feature SelectionAbstract
Kritik dan komentar yang disampaikan masyarakat Indonesia terkait kebijakan pemerintah mengenai pembelajaran tatap muka di masa pandemi Covid-19 menuai pro dan kontra. Tidak sedikit orang tua yang khawatir dengan kebijakan ini dikarenakan para orang tua masih takut akan penyebaran klaster baru Covid-19 di Indonesia yang semakin berkembang, di sisi lain banyak juga orang yang beropini hal ini baik diterapkan mengingat pembelajaran secara daring dinilai kurang efektif karena banyak siswa yang sulit menerima materi yang disampaikan guru secara daring serta banyaknya siswa yang belum memiliki perangkat yang memadai. Banyaknya opini yang dituliskan di Twitter membutuhkan pengklasifikasian sesuai sentimen yang dimiliki agar mudah untuk mendapatkan kecenderungan opini tersebut apakah cenderung beropini netral, positif maupun negatif. Analisis sentimen dalam penelitian ini dilakukan dengan menggunakan metode Naïve Bayes dan seleksi ciri Chi Square dalam melakukan klasifikasi. Hasil analisis yang dari metode Naïve Bayes dengan seleksi ciri chi Square memiliki akurasi 93% dan tanpa seleksi ciri Chi Square memiliki akurasi 92%, sehingga dapat disimpulkan Metode Naïve Bayes dengan seleksi ciri Chi Square memiliki tingkat akurasi yang lebih baik dibanding tanpa menggunakan seleksi ciri Chi Square.
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