The results indicate that the Random Forest algorithm performs well in automatically classifying user sentiment in mobile banking application reviews and can be effectively utilized as a tool to support user satisfaction analysis.
The analysis of feature importance shows that positive sentiment is dominated by words related to delivery speed and price, while negative sentiment is dominated by complaints about system errors, sellers, and refund processes, which can be used by application managers to prioritize service improvements.
Sopi Sapriadi, Rahmatia Wulan Dari, Y. Eirlangga· Jurnal Teknologi Dan Sistem...· 0 citations
Overall, the study confirms that Bi-LSTM is a suitable deep learning approach for sentiment classification of application reviews and offers meaningful insights that can support Duolingo developers in evaluating user opinions and enhancing application quality.
Muhammad Nasrullah, Abdul Azis, Intan Mila Hakim· JURNAL ILMIAH SAINS TEKNOLOG...· 0 citations
This study aims to classify the sentiment of Alfagift user reviews into three classes: Positive, Negative, and Neutral, using the Support Vector Machine (SVM) method with Term Frequency-Inverse Document Frequency (TF-IDF) feature representation.
Nazwa Asyifa, S. Susanto· Jurnal Teknologi Informasi d...· 0 citations
The sentiment of IKD user reviews is analyzed and the effectiveness of Support Vector Machine (SVM) and Random Forest is compared to provide a data-driven basis for the Directorate General of Population and Civil Registration to prioritize feature improvements and enhance IKD service quality.
Atiqa Auliana Fitri, Y. Yuhandri, Rini Sovia· Journal of Science and Socia...· 0 citations
Investigation of user sentiment toward the Threads app through review classification indicates that the method used is capable of identifying user opinions and can be used as a basis for evaluating improvements in app service quality.
The study aimed to analyze consumer review sentiment and compare the performance of K-Nearest Neighbor (K-NN), Naïve Bayes (NB), and Support Vector Machine (SVM) algorithms in classifying positive, negative, and neutral sentiments.
Ameylan Verina Tabun, Hairani Hairani, Dadang Priyanto· Jurnal Ilmu Komputer dan Tek...· 0 citations
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