Abstract
Monitoring public concern in the surrounding environment to certain events is done to address changes in public behavior individually and socially. The results of monitoring public attention can be used as a benchmark for related parties in making the right policies and strategies to deal with changes in public behavior as a result of the COVID-19 pandemic. Monitoring public attention can be done using Twitter social media data because the users of the media are quite high, so that they can represent the aspirations of the general public. However, Twitter data contains varied topics, so a classification process is required to obtain data related to COVID-19. Classification is done by using word embedding variations (Word2Vec and fastText) and deep learning variations (CNN, RNN, and LSTM) to get the classification results with the best accuracy. The percentage of COVID-19 data based on the best accuracy is calculated to determine how high the public's attention is to the COVID-19 pandemic. Experiments were carried out with three scenarios, which were differentiated by the number of data trains. The classification results with the best accuracy are obtained by the combination of fasText and LSTM which shows the highest accuracy of 97.86% and the lowest of 93.63%. The results of monitoring public attention to the time vulnerability between June and October show that the highest public attention to COVID-19 is in June.
Highlights
Monitoring public concern in the surrounding environment to certain events is done to address changes in public behavior individually and socially
The results of monitoring public attention can be used as a benchmark for related parties in making the right policies and strategies to deal with changes in public behavior as a result of the COVID-19 pandemic
Monitoring public attention can be done using Twitter social media data because the users of the media are quite high, so that they can represent the aspirations of the general public
Summary
Pemantauan perhatian publik di lingkungan sekitar terhadap suatu kejadian tertentu merupakan satu hal yang penting dilakukan. Surabaya yang sering menjadi tempat rujukan Penelitian ini memanfaatkan word embedding sebagai masyarakat untuk mendapatkan berbagai macam metode ekstraksi fitur yang dikombinasikan dengan informasi di wilayah Surabaya adalah akun Twitter metode deep learning. Kendala tersebut menyebabkan dibutuhkannya proses klasifikasi untuk sebagai ekstraksi fitur dan menunjukkan hasil yang unggul jika dibandingkan dengan metode deep learning tanpa word embedding. Dari berbagai literatur tersebut maka penelitian ini akan membandingkan kinerja variasi deep learning (CNN, RNN, dan LSTM) yang dikombinasikan dengan variasi word embedding (Word2Vec dan fastText). Hal tersebut dibuktikan dengan penelitian terdahulu yang telah dilakukan seperti penelitian [7][8] yang menunjukkan bahwa nilai recall, f-measure dan precision lebih tinggi dari beberapa penelitian sebelumnya yang menggunakan machine learning saat diaplikasikan pada objek yang sama. Penelitian ini mengusulkan pemantauan perhatian publik berdasarkan teks Twitter dengan klasifikasi teks menggunakan variasi deep learning dan variasi word embedding. Praposes Data dijadikan pihak terkait dalam membangun kebijakankebijakan dan strategi tanggap darurat yang tepat untuk menghadapi perubahan perilaku publik sebagai efek pandemi
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