Analisis Sentimen Opini Publik Mengenai Vaksin COVID-19 Pada Twitter Menggunakan Metode K-Nearest Neighbor

Asmi, Elkana Putra (2021) Analisis Sentimen Opini Publik Mengenai Vaksin COVID-19 Pada Twitter Menggunakan Metode K-Nearest Neighbor. S1 Sistem Informasi thesis, STMIK Widya Cipta Dharma.

	Text 1741094-S1-Jurnal.pdf Restricted to Repository staff only Download (492kB) | Request a copy
	Text 1741094-S1-Sistem Informasi.pdf Restricted to Repository staff only Download (3MB) | Request a copy

Abstract

Elkana Putra Asmi. 2021, Analisis Sentimen Opini Publik Mengenai Vaksin Covid-19 Pada Twitter Menggunakan Metode K-Nearest Neighbor, Program Studi Sistem Informasi, Sekolah Tinggi Manajemen Informatika dan Komputer (STMIK) Widya Cipta Dharma Samarinda, Pembimbing (I) H. Tommy Bustomi, S.Kom., M.Kom Pembimbing (II) Muhammad Fahmi, S.Kom., M.Kom. Kata kunci : Analisis Sentimen, Klasifikasi, K-Nearest Neighbour, Twitter, Opini Publik, Vaksin COVID-19. Penelitian ini dilakukan untuk mengetahui sentimen opini publik terhadap penyediaan vaksin COVID-19 pada Twitter yang nantinya penelitian ini diharapkan dapat menjelaskan hasil sentimen opini publik mengenai vaksin COVID-19 yang terdiri dari sentimen positif, netral, dan negatif serta dapat digunakan sebagai literatur penelitian lainnya. Pada penelitian ini, proses pengumpulan data yang digunakan yaitu menggunakan library tweetscraper sebagai sarana untuk melakukan pengumpulan data twit. Dalam penelitian ini menggunakan metode K-Nearest Neighbour, dengan perangkat lunak pendukung yang digunakan adalah Microsoft Excel dan Miniconda serta perangkat lunak utama yang digunakan adalah RapidMiner. Adapun hasil akhir penelitian, dari sejumlah 582 data twit yang didapatkan diketahui nilai k = 9 yang diterapkan di setiap rasio menghasilkan nilai rata-rata akurasi dan nilai rata-rata precision tertinggi yakni 59.75% dan 57.90%. Sedangkan nilai k = 5 diterapkan di setiap rasio menghasilkan nilai rata-rata recall tertinggi yakni 51.10%. Metode K-Nearest Neighbour dengan nilai k = 9 menghasilkan kinerja yang lebih baik daripada nilai k = 3, k = 5, dan k = 7. Didapatkan pula persentase sentimen netral sebanyak 50%, sentimen positif sebanyak 29%, dan sentimen negatif sebanyak 21%. ========================================================= Elkana Putra Asmi. 2021, Analysis of Public Opinion Sentiment Regarding the COVID-19 Vaccine on Twitter Using the K-Nearest Neighbor Method, Information Systems Study Program, College of Informatics and Computer Management (STMIK) Widya Cipta Dharma Samarinda, Supervisor (I) H. Tommy Bustomi, S.Kom., M.Kom Advisor (II) Muhammad Fahmi, S.Kom., M.Kom. Keywords: Sentiment Analysis, Classification, K-Nearest Neighbor, Twitter, Public Opinion, COVID-19 Vaccine. The researcher conducted this research to determine the sentiment of public opinion on the provision of the COVID-19 vaccine on Twitter. Later this research is expected to explain the results of public opinion sentiment regarding the COVID-19 vaccine, which consists of positive, neutral, and negative sentiments and can be used as other research literature. In this study, the data collection process used is using the tweet scraper library to collect Twitter data. This study using the K-Nearest Neighbor method, the supporting software used is Microsoft Excel and Miniconda. The main software used in RapidMiner. As for the final result of the research, from several 582 twit data obtained, it is known that the value of k = 9, which is applied in each ratio, produces an average accuracy value, and the highest precision average value is 59.75% and 57.90%. While the value of k = 5 applied to each ratio resulted in the highest average recall value of 51.10%. The K-Nearest Neighbor method with a value of k = 9 produces better performance than the values of k = 3, k = 5, and k = 7. And then, it is known that the percentage of neutral sentiment is 50%, positive sentiment is 29%, and negative sentiment is 21. %.

Item Type:	Thesis (S1 Sistem Informasi)
Additional Information:	Pembimbing 1 : H. Tommy Bustomi, S.Kom., M.Kom Pembimbing 2 : Muhammad Fahmi, S.Kom., M.Kom
Uncontrolled Keywords:	Analisis Sentimen, Klasifikasi, K-Nearest Neighbour, Twitter, Opini Publik, Vaksin COVID-19
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Sistem Informasi
Depositing User:	Mr Elkana Putra Asmi
Date Deposited:	23 Aug 2021 00:42
Last Modified:	23 Aug 2021 00:42
URI:	http://repository.wicida.ac.id/id/eprint/3897

Actions (login required)

View Item