PERBANDINGAN KINERJA METODE PEMBELAJARAN MESIN UNTUK ANALISIS SENTIMEN ULASAN FILM
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Abstract
Analisis dari sentimen dapat membantu pembuat film dan studio membentuk strategi marketing, menilai kualitas film, analisis kompetisi, dan menganalisa tren yang lebih luas dalam industri film. Sentimen analisis ini dilakukan pada 50.000 ulasan film di IMDB dan menggunakan metode K-Nearest Neighbor, Naïve Bayes, dan Random Forest untuk mengklasifikasi positif atau negatifnya sentiment sebuah ulasan. Ulasan dalam format teks diubah menjadi representasi vector dengan menggunakan TF-IDF (term frequency–inverse document frequency) vectorizer. K-Nearest Neighbor (K-NN) menghasilkan akurasi terbaik 74%, Naïve Bayes (NB) menghasilkan akurasi 85%, dan Random Forest (RF) menghasilkan akurasi 85%. Naïve Bayes dan Random Forest mendapatkan hasil dengan akurasi terbaik. Naïve Bayes memprediksi ulasan negatif yang lebih akurat, sedangkan Random Forest memprediksi ulasan positif yang lebih akurat.
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