Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile

Authors

  • Moh. Ainur Rohman Universitas Islam Negeri Maulana Malik Ibrahim Malang
  • - Suhartono Universitas Islam Negeri Maulana Malik Ibrahim Malang
  • Totok Chamidy Universitas Islam Negeri Maulana Malik Ibrahim Malang

DOI:

https://doi.org/10.33633/tc.v22i2.7876

Keywords:

Attention mechanism, BiGRU, PLN Mobile, Sentiment analysis, Word2vec

Abstract

PLN Mobile adalah aplikasi ponsel customer self-service yang terintegrasi dengan Aplikasi Pengaduan dan Keluhan Pelanggan (APKT) dan Aplikasi Pelayanan Pelanggan Terpusat (AP2T). Mulai awal tahun 2021 sampai sekarang PLN menggencarkan sosialisasi PLN Mobile pada masyarakat sehingga jumlah ulasan PLN Mobile pada google playstore meningkat drastis. Untuk mengetahui kepuasan pelanggan tidak bisa hanya dengan melihat dan menganalisis dari kolom ulasan PLN Mobile di google playstore, hal ini dikarenakan data ulasan berbentuk tidak terstruktur. Untuk mengatasi masalah ini dibutuhkan teknik khusus yaitu analisis sentimen. Penelitian ini bertujuan untuk mengusulkan arsitektur analisis sentimen untuk mengatasi ketidakmampuan algoritma deep learning seperti LSTM dan GRU dalam menangkap informasi penting. Arsitektur yang diusulkan yaitu mengkombinasikan Bidirectional GRU (BiGRU) dengan attention mechanism menggunakan word2vec sebagai word embedding. Attention mechanism digunakan untuk menangkap kata yang penting sehingga arsitektur tersebut dapat memahami informasi yang penting. Kemudian, arsitektur yang diusulkan dilakukan perbandingan dengan metode CNN, CNN-GRU, CNN-LSTM, CNN-BiGRU, CNN-BiLSTM dengan menggunakan data ulasan PLN Mobile. Hasil eksperimen menunjukkan bahwa arsitektur analisis sentimen yang diusulkan memiliki akurasi dan f1-score yang lebih tinggi.

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Published

2023-05-26

Issue

Vol. 22 No. 2 (2023): Mei 2023

Section

Articles

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