PERBANDINGAN PERFORMANSI MODEL NAIVE BAYES DALAM MENGANALISIS SENTIMEN MENGENAI E-SPORT MOBILE LEGENDS
Abstract
Analisis sentimen merupakan salah satu metode dalam pemrosesan bahasa alami yang digunakan untuk mengidentifikasi dan mengklasifikasikan opini atau emosi dalam suatu teks. Dalam penelitian ini, dilakukan perbandingan tiga model Naïve Bayes—Multinomial Naïve Bayes, Gaussian Naïve Bayes, dan Bernoulli Naïve Bayes—untuk menganalisis sentimen komentar live chat turnamen E-Sports Mobile Legends di YouTube. Data dikumpulkan dari kolom live chat siaran grand final MSC 2023, kemudian dilakukan preprocessing dengan teknik tokenisasi, penghapusan stopwords, stemming, dan transformasi TF-IDF. Ketiga model Naïve Bayes diterapkan pada dataset yang telah diproses, dan kinerjanya dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa Bernoulli Naïve Bayes memberikan performa terbaik dengan akurasi dan F1-score tertinggi dibandingkan dengan model lainnya. Hal ini menunjukkan bahwa pendekatan Bernoulli lebih optimal dalam menangani karakteristik teks komentar live chat yang cenderung pendek dan mengandung banyak istilah informal. Temuan ini dapat menjadi acuan dalam penerapan analisis sentimen untuk data teks tidak terstruktur dalam industri E-Sports.
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