ASTIANA, ASTIANA (2022) ANALISIS SENTIMEN TERHADAP PENERIMAAN BANTUAN SOSIAL TUNAI PADA TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES DAN SUPPORT VECTOR MACHINE. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
This research is based on the impact of covid-19, which is a new virus that has just emerged transmitted to the surface during the last two years defined by the international health agency (WHO), This prompted the government to take action by improving health protocols and providing economic assistance. Cash Social Assistance (BST) is one of the assistance from the government in the form of cash to the poor who have been affected by the COVID-19 impact, which is part of the Social Assistance program. This led to positive and negative responses from the public which were poured on Twitter social media. In this study, sentiment analysis was conducted to determine public opinion on the acceptance of aid. The opinion was obtained through the Twitter platform with the keywords being “BST” “BLT” “Cash Social Assistance” “Social Assistance” “Bansos” “BANSOS”. Here the method used is the Naive Bayes classification algorithm and the Support Vector Machine. The text labeling process is done automatically using textblob. The results showed that the Naive Bayes algorithm had a better performance when compared to the SVM algorithm with a value of 87 percent. Key words: BST, Naive Bayes, SVM, TextBlob. Penelitian ini berdasarkan dari dampak covid – 19 yang merupakan virus baru yang baru muncul bertransmisi ke permukaan selama dua tahun terakhir yang ditetapkan oleh badan kesehatan internasional (WHO), hal tersebut membuat pemerintah memberikan tindakan dengan meningkatkan protokol kesehatan dan memberikan bantuan ekonomi. Bantuan Sosial Tunai (BST) merupakan salah satu bantuan dari pemerintah berupa uang tunai kepada masyarakat miskin yang terkana dampak dari covid – 19, yang berupakan bagian dari program Bantuan Sosial. Hal ini menimbulkan respon positif dan negatif dari masyarakat yang dituangkan pada media sosial Twitter. Pada penelitian ini dilakukan analisa sentimen untuk mengetahui opini masyarakat terhadap penerimaan bantuan. Opini tersebut didapatkan melalui platform Twitter dengan kyeword adalah “BST” “BLT” “Bantuan Sosial Tunai” “Bantuan Sosial” “Bansos” “BANSOS”. Disini metode yang digunakan yaitu algoritma klasifikasi Naive Bayes dan Support Vector Machine. Proses pelabelan teks dilakuka secara otomatis menggunakan textblob. Hasil penelitian menunjukan bahwa algoritma Naive Bayes memiliki performa yang lebih baik jika dibandingkan dengan algoritma SVM dengan nilai sebesar 87%. Kata kunci: BST, Naive Bayes, SVM, TextBlob.
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