User Requirement Recommendation Model for Waste Reporting Platforms Based on UX Topics and Sentiment Analysis
DOI:
https://doi.org/10.30871/jaic.v9i6.11371Keywords:
LDA, Rule-Based Recommendation, SVM, UX Honeycomb, Waste ManagementAbstract
Waste management remains a critical issue in Indonesia, as emphasized in the RPJMN 2025–2029. Ineffective collection and processing services, coupled with limited public participation, continue to hinder progress. Meanwhile, social media has emerged as a primary channel for citizens to express complaints and reports on waste, yet the unstructured nature of comments poses challenges for integration into official reporting systems. This study proposes a user requirements recommendation model based on social media data by integrating sentiment analysis, topic modeling, and rule-based recommendation. Data were collected from YouTube and TikTok comments. Sentiment classification was performed using Support Vector Machine (SVM), while Latent Dirichlet Allocation (LDA) was employed for topic modeling, with results mapped onto the UX Honeycomb dimensions. Recommendation rules were then formulated by combining sentiment polarity with UX dimensions. The SVM model achieved an average accuracy of 87.5% with balanced precision, recall, and F1-score. LDA produced 15 coherent topics, which were distributed across seven UX dimensions. The integration revealed that the main user requirements include transparency in report follow-up through real-time notifications and clear status updates. Additional recommendations involve simplifying the reporting process, providing auto-fill features, improving visual design, and establishing a user appreciation system. The findings demonstrate the potential of leveraging social media comments to systematically capture user requirements, offering practical insights for developers to design waste reporting platforms that are effective, user-friendly, and responsive to community expectations.
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