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	bibo:abstract "The implementation of the New Generation Academic Information System (SIAKANG) at Universitas Sultan Ageng Tirtayasa has faced various student complaints regarding service quality, with specific issues including technical \r\ndifficulties in the login process, inaccuracies in academic grade data, slow loading times, frequent system errors, and a non-responsive user interface on mobile devices. This study aims to classify student opinions through sentiment analysis, \r\nidentify key problem topics, and formulate structured service improvement recommendations based on user needs. The method employed is a quantitative approach with data collection via open-ended questionnaires (104 respondents), \r\nprocessed through text preprocessing stages, Term Frequency–Inverse Document Frequency (TF–IDF) numerical representation, and clustering using the K-Means algorithm. The determination of the optimal number of clusters via the Elbow Method resulted in k=3, with a Silhouette Score validation of 0.111, which was then integrated into the Quality Function Deployment (QFD) method through the construction of the House of Quality (HoQ) matrix. The study concludes that although the combination of TF-IDF and K-Means was not optimal in separating sentiment polarity due to data imbalance, the method was highly effective in mapping system weaknesses. The primary priorities for SIAKANG service improvement include strengthening system security, periodic academic data audits, server infrastructure optimization, and user interface/experience (UI/UX) \r\nenhancements to sustainably improve the quality of academic information services."^^xsd:string;
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