Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines
DOI:
10.33395/sinkron.v8i1.12063Keywords:
Classification, Flood, Sentiment Analysis, SVM, Text Mining, TwitterAbstract
Floods can cause negative impacts in various aspects, starting from economic, social, to health aspects. Even quoted from the site gis.bnpb.go.id, during 2022, there have been 1031 cases of flood disasters in Indonesia. Meanwhile in Jakarta, in 2022 there have been 14 cases of floods that caused hundreds of people to lose their homes. Several approaches can be taken to determine public opinion about flooding, one of which is text mining with an analysis of community sentiment. Sentiment analysis aims to determine public opinion regarding flood management in the capital city of Jakarta based on positive, neutral, and negative categories. To get the public sentiment, researchers carried out several stages, including the preprocessing stage. After obtaining public sentiment regarding flood management through the preprocessing stage, then classification is carried out based on public opinion so that it can be used by related parties for evaluation material in flood handling. In this study, the classification method used is the SVM method which is one of the supervised learning methods in machine learning. After classification, the next stage is the testing process using the K-Fold Cross Validation method. From the various sentiments obtained from Twitter data, it can be concluded that there are around 414 positive sentiments and 2464 negative sentiments related to flood handling in DKI Jakarta, while the results obtained from the test results show that the accuracy reaches 88.6%, the precision reaches 88.6% and recall reached 89.4%.
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Copyright (c) 2023 M Amiruddin Saddam, Erno Kurniawan Dewantara, Achmad Solichin
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Muhammad Khoiruddin harahap
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