Implementasi Bidirectional LSTM untuk Analisis Sentimen Terhadap Layanan Grab Indonesia

Author:

Alghifari Dloifur Rohman,Edi Mohammad,Firmansyah Lutfi

Abstract

Grab Indonesia is one of the leading online motorcycle taxi companies in Indonesia and has a large number of customers in Indonesia. The level of customer satisfaction varies with the services provided, so there must be suggestions and complaints from customers. Sentiment analysis can be used as a solution to determine the level of service satisfaction in order to improve the system and service. This study aims to determine the level of satisfaction of Grab Indonesia users through the Grab application in the Playstore. One of the approaches that can be used is LSTM. LSTM is an RNN algorithm development to solve the vanishing gradient problem. LSTM has the disadvantage of only running can only capture information from one direction. Bidirectional LSTM (BiLSTM) is an LSTM method that has been developed, where BiLSTM can capture information from two directions. In this BiLSTM method, the more data, the better the algorithm's performance. The test results show that BiLSTM is more reliable than LSTM in the case of sentiment analysis on the Indonesian Grab service. BiLSTM produces the best accuracy of 91% and training loss of 28%. Suggestions for future research can produce more and varied word representations by considering the word embedding combinations.

Publisher

Universitas Komputer Indonesia

Subject

General Earth and Planetary Sciences,General Environmental Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Effectiveness of Deep Learning Methods CNN - Bi-LSTM and GloVe in Sentiment Analysis of MyTelkomsel Application Reviews;2024 International Conference on Data Science and Its Applications (ICoDSA);2024-07-10

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