Penerapan Metode Long Short-Term Memory dan Word2Vec dalam Analisis Sentimen Ulasan pada Aplikasi Ferizy

Authors

  • Mega Vebika Shyahrin Telkom University
  • Yuliant Sibaroni Telkom University
  • Diyas Puspandari Telkom University

DOI:

https://doi.org/10.33633/tc.v22i4.9205

Keywords:

Analisis Sentimen, Long Short-Term Memory, Word2Vec

Abstract

Tranportasi merupakan hal yang penting bagi masyarakat dalam mobilitas sehari-hari. Karena memiliki peranan penting dan dapat memudahkan kehidupan masyarakat, pemerintah mulai mengoptimalkan pembangunan sarana transportasi dan memulai inovasi digital, salah satunya pada moda transportasi laut. Perseroan Terbatas Angkutan Sungai, Danau, dan Penyeberangan Indonesia (PT ASDP)  meluncurkan aplikasi Ferizy pada Google Playstore. Dalam inovasi ini, sentimen masyarakat dapat membantu untuk mengetahui kepuasan, kekurangan, saran, dan kritik. Terkait hal tersebut maka diperlukan analisis sentimen untuk memahami maksud ulasan. Analisis ini mengekstrak data ulasan lalu mengolah data tekstual secara otomatis untuk mendapatkan makna sentimen yang terkandung dalam ulasan. Penelitian ini mengimplementasikan klasifikasi Long Short-Term Memory (LSTM) dan ekstraksi fitur Word2Vec variasi skip-gram serta CBOW pada dataset ulasan aplikasi Ferizy. Hasil pengujian dari model menghasilkan nilai akurasi sebesar 88,20% untuk variasi skip-gram dan 74,20% untuk variasi CBOW.

Author Biographies

Mega Vebika Shyahrin, Telkom University

School of Computing

Yuliant Sibaroni, Telkom University

School of Computing

Diyas Puspandari, Telkom University

School of Computing

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Published

2023-11-21

Issue

Vol. 22 No. 4 (2023): November 2023

Section

Articles

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