Sentiment Analysis and Classification of Forest Fires in Indonesia


Indra Irawanto(1); Cynthia Widodo(2); Atin Hasanah(3); Prema Adhitya Dharma Kusumah(4); Kusirini Kusrini(5*); Kusnawi Kusnawi(6);

(1) AMIKOM Yogyakarta of University
(2) AMIKOM Yogyakarta of University
(3) AMIKOM Yogyakarta of University
(4) AMIKOM Yogyakarta of University
(5) AMIKOM Yogyakarta of University
(6) AMIKOM Yogyakarta of University
(*) Corresponding Author

  

Abstract


Twitter is a well-known social media platform since it allows users to retweet, leave comments, exchange the latest information, and even find out about forest fires. However, no one has processed Twitter data in the form of the topic of forest fires. Despite the fact that this information is incredibly important for determining how much people care about sharing this knowledge and this phenomenon. Hence, one of the efforts in managing Twitter data in the form of text is using NLP (Natural Language Processing) which is now starting to be widely discussed. In addition, the use of word weighting utilizing Vader will also be used in this process. Furthermore, the use classifying process is conducted using 3 kinds of algorithms including Naïve Bayes, Random Forest and SVM (Support Vector Machine). The results of this study, the accuracy obtained from each method has not reached 90%. The Precision, Recall and F1-Score values have also not reached 90%.

Keywords


Sentiment Analysis; Forest fires; Naive Bayes; Random Forest; SVM (Support Vector Machine).

  
  

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doi  https://doi.org/10.33096/ilkom.v15i1.1337.175-185
  

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