Analisis Sentimen pada Data Ulasan Twitter dengan Long-Short Term Memory

  • Sharfina Febbi Handayani Politeknik Harapan Bersama
  • Riszki Wijayatun Pratiwi Politeknik Harapan Bersama Tegal
  • Dairoh Dairoh Politeknik Harapan Bersama Tegal
  • Dwi Intan Af’idah Politeknik Harapan Bersama Tegal
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DOI https://doi.org/10.31544/jtera.v7.i1.2022.39-46
Published 2022-06-26
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Abstract

Perkembangan media sosial telah memudahkan masyarakat dalam menyebarluaskan informasi. Salah satu bentuk informasi yang dimaksud berupa kebebasan dalam menyampaikan opini di media sosial. Perkembangan penelitian terkait analisis sentimen pada data ulasan teks bertujuan untuk mengetahui polaritas opini di media sosial yang telah mengalami peningkatan. Salah satu metode yang diterapkan dalam analisis sentimen teks ulasan yaitu penggunaan metode Long Short-Term Memory (LSTM). Tujuan penelitian ini adalah untuk mengetahui performa model LSTM terhadap berbagai sentimen ulasan teks Twitter berbahasa Indonesia. Proses pengujian dilakukan berdasarkan perhitungan dari nilai akurasi tuning hyperparameter. Pengujian akurasi penelitian ini menggunakan parameter Word2Vec, fungsi aktivasi, jumlah epoch, dan jumlah neuron. Hasil pengujian kinerja LSTM yang optimal dari penelitian ini diperoleh berdasarkan tuning arsitektur Word2Vec Continuous Bag of Words (CBOW) dengan akurasi 57,15%, tuning jumlah neuron sebanyak 150 menghasilkan nilai akurasi 57,35%, tuning jumlah epoch sebesar 30 menghasilkan nilai akurasi 57,40%, serta tuning fungsi aktivasi softmax menghasilkan nilai akurasi sebesar 57,35%.

Keywords

sentimen ulasan Twitter LSTM Word2Vec

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Issue

Vol. 7 No. 1: June 2022

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How to Cite

[1]
“Analisis Sentimen pada Data Ulasan Twitter dengan Long-Short Term Memory”, JTERA, vol. 7, no. 1, pp. 39–46, Jun. 2022, doi: 10.31544/jtera.v7.i1.2022.39-46.