<![CDATA[SP4N LAPOR! is a vital channel for public aspirations and complaints. Understanding public sentiment towards this service is crucial for quality improvement. This study aims to analyze public sentiment towards SP4N LAPOR! through data triangulation from various sources. Sentiment analysis using a Large Language Model (LLM) was applied to comment data from Instagram (lapor1708) and review data of the SP4N LAPOR! app on the Google Play Store. The results of this quantitative analysis were then triangulated with qualitative findings from in-depth interviews with users. The research consistently shows a dominance of negative sentiment, with only a few positive sentiments. The main complaints driving this negative sentiment are the slow response or follow-up and the lack of transparency regarding the status of reports. Additional insights from triangulation also revealed various technical issues with the platform. In conclusion, although some aspects of the service were rated positively or neutrally, the overall public perception tends to be negative, highlighting crucial areas for improvement in response speed, communication of report statuses, and platform technical stability. This study emphasizes the importance of multi-source analysis for a comprehensive understanding of public service user feedback.]]>
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