Analisis Sentimen dan Pemodelan Topik Ulasan Pengguna terhadap Kepercayaan, Kualitas Layanan Elektronik, Keadilan Harga, dan Pengalaman Pelanggan pada Aplikasi Maxim di Google Play Store
DOI:
https://doi.org/10.30640/jumma45.v5i2.8188Keywords:
Customer Experience, Maxim, Sentiment Analysis, Text Mining, Topic ModelingAbstract
The increasingly competitive ride-hailing industry in Indonesia highlights the need to understand user perceptions through application reviews. This study aims to analyze sentiment and identify dominant topics in Maxim user reviews on Google Play Store related to trust, e-service quality, price fairness, and customer experience, and to examine the relationship between sentiment and user ratings across these aspects. A quantitative text mining approach was applied to 539,645 raw reviews collected through web scraping. From the most recent 24-month period, 4,985 reviews were selected using proportional stratified sampling. The analysis involved text preprocessing, aspect-based theme extraction, Latent Dirichlet Allocation (LDA) for topic modeling, sentiment classification using Naïve Bayes and Support Vector Machine (SVM), and Pearson correlation testing. Sentiment labels were assigned using manual anatomi. The results show that customer experience was the most dominant theme (59.5%), with 74.0% positive sentiment, while e-service quality had the highest proportion of negative sentiment (32.8%). The SVM model achieved an accuracy of 93.78%, compared with 76.53% for Naïve Bayes. All four aspects showed significant positive correlations between sentiment and user ratings (r = 0.466–0.653; p < 0.001), with customer experience exhibiting the strongest correlation. These findings provide empirical insights into user perceptions of digital transportation services through review-based text analysis and offer practical input for prioritizing service improvements, particularly in e-service quality.
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