Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Methods

Erika  Damayanti; Anik Vega Vitianingsih; Slamet  Kacung; Hengki  Suhartoyo; Anastasia  Lidya Maukar

doi:10.51454/decode.v4i2.478

Authors

Erika Damayanti Teknik Informatika Universitas Dr. Seotomo
Anik Vega Vitianingsih Teknik Informatika Universitas Dr.Soetomo
Slamet Kacung Teknik Informatika Universitas Dr. Seotomo
Hengki Suhartoyo Teknik Informatika Universitas Dr. Seotomo
Anastasia Lidya Maukar Teknik Industri President University

DOI:

https://doi.org/10.51454/decode.v4i2.478

Keywords:

Alfagift Apps, LSTM, Sentiment Analysis, Service Quality, SVM

Abstract

The rapid advancement of mobile apps has emerged as an important aspect of the routine of internet-connected users. In Indonesia, many companies are introducing their apps to improve the quality of service for users, and Alfamart is one of them. However, users have identified many shortcomings in these apps. This feedback is provided by users on the review feature of the Alfagift app on the Google Play Store. This research aims to apply a sentiment analysis approach to identify the application's shortcomings so that developers can understand the aspects that need to be improved to improve the quality of application services. The research stages include data collection, preprocessing, labeling, weighting, classification of LSTM and SVM methods, and performance evaluation using a confusion matrix. The dataset consists of 1000 reviews obtained through web scraping techniques. This research uses the Lexicon-based method to classify the dataset into positive, negative, and neutral categories. The analysis results show that 801 data are classified as positive sentiment, 77 as negative sentiment, and 122 as neutral sentiment. Based on testing, both SVM and LSTM methods show good performance. The best accuracy results were obtained using the SVM method, which amounted to 83.5%. Meanwhile, the LSTM method achieved an accuracy of 82%.

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