ANALISIS SENTIMEN MENGGUNAKAN ARSITEKTUR LONG SHORT-TERM MEMORY (LSTM) TERHADAP FENOMENA CITAYAM FASHION WEEK

Laina Farsiah; Alim Misbullah; Husaini Husaini

doi:10.22373/cj.v6i2.14687

ANALISIS SENTIMEN MENGGUNAKAN ARSITEKTUR LONG SHORT-TERM MEMORY (LSTM) TERHADAP FENOMENA CITAYAM FASHION WEEK

Laina Farsiah, Alim Misbullah, Husaini Husaini

Abstract

Analisis sentimen pada teks bertujuan untuk melihat sebuah teks mengandung emosi positif, negatif, atau netral. Hasil analisis dapat digunakan sebagai bahan pertimbangan untuk mengambil keputusan terhadap sebuah isu. Seperti fenomena Citayam Fashion Week yang ramai diperdebatkan di Indonesia, khususnya pada bulan Juli 2022, sangat dibutuhkan analisis sentimen terhadap fenomena tersebut. Dataset yang digunakan berasal dari tweet masyarakat Indonesia dengan kata kunci Citayam Fashion Week. Selanjutnya, setiap tweet akan dilabeli dengan kelas positif, negatif, atau netral berdasarkan leksikal bahasa Indonesia. Penelitian ini menghasilkan model yang dapat digunakan untuk memprediksi setiap tweet bahasa Indonesia ke dalam kategori sentimen positif, negatif, atau netral terkait pandangan dan pendapat masyarakat tentang fenomena Citayam Fashion Week. Metode membangun model yang digunakan, yaitu Long Short Term Memory (LSTM). Akurasi model yang dihasilkan menggunakan LSTM cukup baik, yaitu sebesar 88%.

Keywords

sentiment analysis, deep learning, LSTM, tweet, Citayam Fashion Week

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DOI: http://dx.doi.org/10.22373/cj.v6i2.14687