Analisis sentimen terhadap dampak banjir rob dengan menggunakan metode Support Vector Machine
DOI:
https://doi.org/10.24246/itexplore.v4i2.2025.pp233-244Keywords:
Support Vector Machine (SVM), Sentiment Analysis, Tidal FloodAbstract
Tidal flooding is an event of a natural phenomenon when sea water rises to land due to the influence of changes in sea tides, which causes waterlogging around the coastal area. This tidal flood hit the Demak-Semarang area, especially in the Sayung District area, which hampers and impacts community life. The purpose of this analysis is to analyze public sentiment regarding the impact of tidal flooding in Demak Regency using data obtained from social media, and the results of the analysis can be used as an evaluation for the government and related parties to formulate more responsive and effective policies to overcome the problem of tidal flooding. The SVM (Support Vector Machine) method is used to classify sentiment from each data into positive, negative, or neutral categories. The results of the analysis using SVM showed 3580 initial data, after preprocessing, 3147 data were obtained, with sentiment results of 1581 neutral opinions, 1257 negative, and 309 positive. Most opinions are neutral, indicating that people consider tidal flooding as a natural phenomenon and are used to dealing with it. However, significant negative opinions indicate dissatisfaction with the government's handling, while positive opinions are very minimal. SVM showed 84.44 percent accuracy, 86.7 percent precision, and 97.8 percent recall. The study recommends improvements in flood mitigation, assistance for affected communities, and infrastructure improvements.
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