<![CDATA[Abstract Hydrometeorological disasters often trigger public responses on social media, so it’s requiring analysis public opinion toward such disaster. This study compares the performance of Decision Tree and Naïve Bayes Algorithm to analyze performance on analyzing sentiments of respond related to hydrometeorological disaster. The dataset that used contains of 11890 comments. Comments labeled into Positive, Negative, and neutral then processed by preprocessing, TF-IDF weighting, and classification with 5-fold cross validation. Validation results Naïve Bayes had mean accuracy by 0.79 which higher than Decision Tree, which scored 0,65. Naïve Bayes also got 0.79 score on accuracy, precision, recall, and F1-Score compares to Decision tree that scored scored 0.63, 0.70, 0.63, and 0.64 respectively. Because the gap of the mean accuracy and test accuracy wasn’t far off, concludes that Naïve Bayes have better generalization than Decision Tree. This result shows that Naïve Bayes is better than Decision Tree on classification of Hydrometeorological Disaster respond. Key Words: Sentiment Analysis; Hydrometeorology Disaster; Naïve Bayes; Decision tree; TF-IDF; Social Media Abstrak Bencana hidrometeorologi memicu berbagai respons masyarakat di media sosial. maka diperlukan analisis untuk memahami opini publik terhadap bencana terkait. Tujuan penelitian ini adalah membandingkan kinerja algoritma Naïve Bayes dan Decision Tree dalam melakukan analisis sentimen terkait bencana Hidrometeorologi. Data digunakan berasal dari TikTok, Instagram Facebook yang terdiri dari 11.890 komentar dengan label kelas positif, negatif, dan netral yang kemudian diproses melalui tahapan pra-pemrosesan, pembobotan TF-IDF, dan klasifikasi dan validasi 5-fold cross validation. Hasil validasi menunjukkan Naïve Bayes memperoleh mean accuracy 0,79 lebih tinggi daripada Decision Tree yang mendapat skor 0,65. Pada data uji, Naïve Bayes memperoleh accuracy, precision, recall, dan F1-Score yang seragam 0,79 lebih unggul dari Decision Tree dengan accuracy 0,63, precision 0,70, recall 0,63, dan F1-score 0,64. Dikarenakan jarak mean acmecuracy dan test accuracy berdekatan disimpulkan Naïve Bayes mempunyai kemampuan generalisasi lebih baik daripada Decision Tree. Hasil ini menunjukkan Naïve Bayes unggul dalam mengklasifikasi sentimen terkait bencana Hidrometeorologi]]>
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