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Affiliations
Ari Syafri
Program Studi Teknik Informatika, Universitas Komputer Indonesia
Azhar Alfaini
Program Studi Teknik Informatika Universitas Komputer Indonesia
Gilang Dhiya Ulhaq
Program Studi Teknik Informatika, Universitas Komputer Indonesia
Mochammad Gymnastiar
Program Studi Teknik Informatika, Universitas Komputer Indonesia
Hidayat Hidayat
Teknik Komputer, Universitas Komputer Indonesia
PENERAPAN SENTIMEN ANALISIS TWITTER TERHADAP COVID-19 MENGGUNAKAN SUPPORT VECTOR MACHINE
Vol 13 No 2 (2024): Komputa : Jurnal Ilmiah Komputer dan Informatika
Abstract
Pemanfaatan media sosial seperti Twitter sangat masif digunakan oleh masyarakat di dunia internet, sehingga informasi yang sedang viral dapat diperoleh dengan cepat. Kondisi pandemi Covid-19 yang telah melanda dunia tentunya dapat menyebabkan perubahan-perubahan pada tatanan kehidupan masyarakat saat ini khususnya di daerah DKI Jakarta. Penelitian ini bertujuan untuk mengetahui respon masyarakat terhadap Covid-19 melalui analisis sentimen menggunakan perbandingan model decision tree, regresi logistik, random forest, dan support vector machine (SVM). Melalui pemanfaatan teknik Text Mining, metode klasifikasi akan menghasilkan sentimen bernilai positif, netral atau negatif. Hasil pengujian menunjukkan bahwa model SVM menjadi model utama dengan rata-rata akurasi sebesar 0,6094 dan standar deviasi terendah sebesar 0,0378. Analisis sentimen diuji dengan 5230 data tweet. Hasil analisis menunjukkan bahwa prosentase sentimen positif (31,64%) lebih besar daripada sentimen negatif (20,48%), walaupun sentimen netral mendominasi dengan prosentase sebesar 47,88%.