NAUFAL ZHAFRAN ALBAQI, . (2022) ANALISIS SENTIMEN MASYARAKAT TERHADAP KEBIJAKAN PPKM PADA MEDIA SOSIAL TWITTER MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER (NBC). Sarjana thesis, UNIVERSITAS NEGERI JAKARTA.
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Abstract
Pemberlakuan Pembatasan Masyarakat (PPKM) adalah salah satu kebijakan pemerintah dalam mencegah penyebaran Covid-19. Penerapan kebijakan PPKM yang telah berlangsung lama, menimbulkan banyak tanggapan di masyarakat. Twitter merupakan salah satu media sosial yang digunakan masyarakat untuk menanggapi penerapan kebijakan PPKM tersebut. Pada penelitian ini akan dilakukan analisis sentimen masyarakat terhadap kebijakan PPKM pada media sosial Twitter dengan menggunakan metode Naive Bayes Classifier (NBC). Selain itu juga, akan dilihat gambaran topik yang sering menjadi perbincangan pada sentimen positif, negatif dan netral dalam penerapan kebijakan PPKM. Metode NBC adalah metode klasifikasi yang didasarkan pada penerapan teorema bayes, metode ini dipilih karena lebih cepat dan sangat baik digunakan untuk pengklasifikasian teks. Hasilnya didapatkan bahwa metode NBC mampu mendapatkan akurasi pada data latih berkisar antara 68% sampai 71%. Sementara itu, tingkat akurasi pada data uji sebesar 71%. Hasil ini menunjukan bahwa algoritma Naive Bayes Classifier memiliki performa yang cukup baik. Pada sentimen negatif topik yang sering menjadi perbincangan perpanjangan PPKM, penamaan PPKM berlevel, penutupan jalan, pembatasan waktu makan dan penerapan sistem kerja dari rumah. Sementara itu pada sentimen positif, topik yang sering dibahas yaitu, penerapan protokol kesehatan yang semakin baik, vaksinasi, serta penurunan level PPKM dan kasus konfirmasi Covid-19. *********************************************************** The implementation of Community Restrictions (PPKM) is one of the policies of the Indonesian government in preventing the spread of Covid-19. The implementation of the PPKM policy that has been going on for a long time has generated many responses in the community. Twitter is one of the social media used by the public to respond to the implementation of the PPKM policy. In this study, an analysis of public sentiment will be carried out on the PPKM policy on Twitter social media using the Naive Bayes Classifier (NBC) method. In addition, an overview of topics that are often discussed on positive, negative, and neutral sentiments in the implementation of PPKM policies will also be seen. The NBC method is a classification method based on the application of Bayes' theorem, this method was chosen because it is faster and very good for text classification. The results showed that the NBC method was able to obtain accuracy on training data ranging from 68% to 71%. Meanwhile, the accuracy rate on the test data is 71%. These results indicate that the Naive Bayes Classifier algorithm has a fairly good performance. On negative sentiment, topics that are often discussed are the extension of PPKM, naming leveled PPKM, road closures, limiting meal times, and implementation of a work from home system. Meanwhile, on positive sentiment, topics that are often discussed are the implementation of better health protocols, and vaccinations, as well as decreasing PPKM levels and decreasing Covid-19 confirmation cases.
| Item Type: | Thesis (Sarjana) |
|---|---|
| Additional Information: | 1). Prof. Dr. Suyono, M.Si.; 2). Danisa Siregar, S.Stat., M.Si |
| Subjects: | Sains > Statistika |
| Divisions: | FMIPA > S1 Statistika |
| Depositing User: | sawung yudo |
| Date Deposited: | 26 Nov 2024 01:52 |
| Last Modified: | 26 Nov 2024 01:52 |
| URI: | http://repository.unj.ac.id/id/eprint/52207 |
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