The results indicate that the RoBERTa model is effective for sentiment analysis of Indonesian-language application reviews, but requires better labeling strategies and data augmentation to improve neutral class performance.
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 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
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.
It is demonstrated that explicit and implicit linguistic complexity is an important determinant of Transformer model performance and contributes to Indonesian-language sentiment analysis by integrating LIME to provide transparent explanations of model predictions for online transportation reviews.
Daffa Putra Emeral· Neptunus· 0 citations
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