ANALISIS SENTIMEN TWITTER MENGENAI KEBIJAKAN EKONOMI PEMERINTAH PADA MASA PANDEMI COVID- 19 MENGGUNAKAN PENDEKATAN NATURAL LANGUAGE PROCESSING

ALFREDA, TANIA (2023) ANALISIS SENTIMEN TWITTER MENGENAI KEBIJAKAN EKONOMI PEMERINTAH PADA MASA PANDEMI COVID- 19 MENGGUNAKAN PENDEKATAN NATURAL LANGUAGE PROCESSING. S1 thesis, Universitas Mercu Buana - Menteng.

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Abstract

Di Indonesia, infeksi COVID-19 pertama kali teridentifikasi pada 2 Maret 2020. Sejak saat itu, jumlah kasus di Indonesia terus meningkat. Pemerintah menanggapi masalah ini dengan mengeluarkan kebijakan yang ditujukan untuk menyelesaikan masalah yang disebabkan oleh infeksi COVID-19. Data Twitter mengenai opini publik tentang kebijakan ekonomi pemerintah dianalisis dalam penelitian ini. Pengumpulan data meliputi 2000 tweet kemudian di preprocessing lalu dan dijalankan melalui metode TF-IDF. Data kemudian dibagi menjadi 80% data testing dan 20% data uji, dan diklasifikasikan menggunakan SVM. Hasilnya adalah nilai akurasi sebesar 86.25%, nilai error sebesar 13.75%, nilai presisi sebesar 88.04%, nilai recall sebesar 95.56%, dan skor F1 sebesar 91.64%. Kata kunci: Analisis Sentimen, TF-IDF, Support Vector Machine, Klasifikasi, Covid-19. In Indonesia, COVID-19 infection was first identified on March 2, 2020. Since then, the number of cases in Indonesia has continued to increase. The government responded to this problem by issuing policies aimed at solving problems caused by COVID-19 infection. Twitter data regarding public opinion about the government's economic policies are analyzed in this study. Data collection includes 2000 tweets, which are then pre-processed and executed using the TF- IDF method. The data is then divided into 80% testing data and 20% testing data, and classified using SVM. The results are an accuracy value of 86.25%, an error value of 13.75%, a precision value of 88.04%, a recall value of 95.56%, and an F1 score of 91.64%. Key words: Sentiment Analysis, TF-IDF, Support Vector Machine, Classification, Covid-19.

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