IMPLEMENTASI PROBABILISTIC NEURAL NETWORK DAN WORD EMBEDDING UNTUK ANALISIS SENTIMEN VAKSIN SINOVAC
DOI:
https://doi.org/10.51977/jti.v3i2.588Keywords:
Word embedding, Probabilistic Neural Network, Sentiment AnalysisAbstract
Penelitian ini bertujuan melakukan implementasi Probabilistic neural network dan Word Embedding dalam kasus sentiment analysis tentang tanggapan masyarakat tentang pemberian vaksin sinovac yangg diunggah di Twitter dan 3 class:positif, negative dan netral. Metode yang dipilih adalah metode klasifikasi Probabilistic Neural Network. Sebelum melakukan klasifikasi, praprocessing pada penelitian ini meliputi tokenizasi, normalisasi, menghilangkan emoticon, Convert Negasi, Stemming, Stopword Removal serta Word embedding. dataset yang digunakan berjumlah 1177 dataset dengan pembagiannya yaitu 560 dataset positif, 355 dataset negative dan 262 dataset netral. Program dirancang menggunakan Bahasa pemrograman python dengan beberapa library seperti keras, tensorflow dan pandas. Akurasi yang didapatkan pada pelatihan menggunakan Probabilistic Neural Network sebesar 91%. Hasil pengujian adalah penelitian ini mampu melakukan sentiment analysis dengan kesalahan sebesar 9%.
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