<![CDATA[This study used sentiment analysis and text mining on the user reviews of the Lelang Indonesia mobile application, specifically focusing on the ratings and reviews on the Google Play Store for the past six years. Sentiment analysis was performed by manually categorizing the data according to user review scores (positive or negative) and subsequently classified using the Support Vector Machine technique to assess accuracy levels. Various text mining approaches were employed to extract details regarding the issues or problems encountered by users. The study found that numerous Lelang Indonesia mobile application users encounter barriers that adversely influence their evaluation of public service quality, as seen by the attitude expressed in their reviews. The analysis of these reviews revealed several concerns, mainly regarding technical applications and service business procedures that require attention and follow-up from institutions. The study's findings can offer institutions a means to evaluate the quality of auction services delivered via mobile applications and serve as a basis for proactively enhancing these services to align with user expectations. This study enhances the understanding of mobile application-based public service satisfaction evaluation, enabling public institutions to evaluate these services in real-time by analyzing user sentiment from reviews.]]>
Copyrights © 2025
