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All Journal Indonesian Journal of Artificial Intelligence and Data Mining
Salsabila Zahirah Pranida
Universitas Islam Indonesia
Computer Science & IT

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Sentiment Analysis of Expedition Customer Satisfaction using BiGRU and BiLSTM Salsabila Zahirah Pranida; Arrie Kurniawardhani
Indonesian Journal of Artificial Intelligence and Data Mining Vol 5, No 1 (2022): March 2022
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v5i1.17361

Abstract

The occurrence of a pandemic caused behavioral changes that occurred in Indonesian society, especially in increasing interest in online purchases. The increased purchases of goods increased the volume of four expeditions, namely: JNE, JNT Express, Sicepat, and Anteraja. To find out the customer satisfaction of the users of the four expeditions automatically, sentiment analysis was conducted based on the thousand tweet data from the opinions of expedition users in three-class categories, which are positive, negative, and neutral. Two deep learning methods were used to analyze the sentiment of expedition customer satisfaction: BiGRU and BiLSTM. The activities conducted during the sentiment analysis were crawling, preprocessing, data labeling, modeling, and evaluation. The performance evaluation results of the two methods use an accuracy matrix over 1,217 test data. The BiGRU method produces an accuracy performance of 71.5% and the BiLSTM method produces an accuracy performance of 66.5%.
Co-Authors Arrie Kurniawardhani