Systematic Literature Review: Analisis Sentimen Berbasis Deep Learning

Penulis

  • Fitroh Fitroh Program Studi Sistem Informasi, UIN Syarif Hidayatullah Jakarta
  • Fahmi Hudaya Program Studi Sistem Informasi, UIN Syarif Hidayatullah Jakarta

DOI:

https://doi.org/10.25077/TEKNOSI.v9i2.2023.132-140

Kata Kunci:

Systematic Literature Review, Analisis Sentimen, Deep Learning

Abstrak

Systematic literature review ini bertujuan untuk mengetahui tren penelitian analisis sentimen berbasis deep learning antara tahun 2020-2023. Fokus kajiannya adalah pada pemahaman tentang pemodelan yang digunakan oleh banyak peneliti, juga nilai akurasi dari masing-masing klasifikasi tersebut. Pertanyaan utama dalam SLR ini yaitu teknik analisis sentimen berbasis deep learning apa yang memberikan akurasi tertinggi. Peneliti menemukan 400 artikel terindeks Scopus dengan menggunakan Publish or Perish 8. Selanjutnya, penyaringan jurnal dan pencarian kluster menggunakan aplikasi Microsoft Excel, Zotero, Mendeley, dan VOS Viewer yang menghasilkan 105 artikel terpilih untuk dianalisis secara deskriptif. Berdasarkan hasil temuan metode yang populer digunakan dalam melakukan analisis sentimen berbasis deep learning dalam jangka waktu yang telah ditentukan adalah metode LSTM dan CNN, baik dilakukan satu metode maupun keduanya. Adapun akurasi tertinggi mencapai 99% dengan rata-rata 89% menggunakan metode LSTM. Pengetahuan ini dapat digunakan untuk mengusulkan model analisis sentimen berbasis deep learning yang memberikan akurasi tertinggi.

Biografi Penulis

Fitroh Fitroh, Program Studi Sistem Informasi, UIN Syarif Hidayatullah Jakarta

Dosen Program Studi Sistem Informasi UIN Syarif Hidayatullah Jakarta

Fahmi Hudaya, Program Studi Sistem Informasi, UIN Syarif Hidayatullah Jakarta

Mahasiswa Program Studi Sistem Informasi UIN Syarif Hidayatullah Jakarta

Referensi

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Unduhan

Diterbitkan

2023-08-31

Cara Mengutip

Fitroh, F., & Hudaya, F. (2023). Systematic Literature Review: Analisis Sentimen Berbasis Deep Learning. Jurnal Nasional Teknologi Dan Sistem Informasi, 9(2), 132–140. https://doi.org/10.25077/TEKNOSI.v9i2.2023.132-140

Terbitan

Vol 9 No 2 (2023): Agustus 2023

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