IMPEMENTASI ALGORITMA BILSTM TERHADAP ANALISIS SENTIMEN OPINI MASYARAKAT INDONESIA MENGENAI PRODUK KECANTIKAN KOREA PADA TWITTER

KURNIADI, TJIOE LOISE JUNIEVA (2023) IMPEMENTASI ALGORITMA BILSTM TERHADAP ANALISIS SENTIMEN OPINI MASYARAKAT INDONESIA MENGENAI PRODUK KECANTIKAN KOREA PADA TWITTER. Other thesis, UNIVERSITAS KHATOLIK SOEGIJAPRANATA.

Preview	Text 19.K1.0033-TJIOE LOISE JUNIEVA KURNIADI-COVER_a.pdf Download (440kB) | Preview
	Text 19.K1.0033-TJIOE LOISE JUNIEVA KURNIADI-BAB I_a.pdf Restricted to Registered users only Download (152kB)
	Text 19.K1.0033-TJIOE LOISE JUNIEVA KURNIADI-BAB II_a.pdf Restricted to Registered users only Download (155kB)
	Text 19.K1.0033-TJIOE LOISE JUNIEVA KURNIADI-BAB III_a.pdf Restricted to Registered users only Download (379kB)
	Text 19.K1.0033-TJIOE LOISE JUNIEVA KURNIADI-BAB IV_a.pdf Restricted to Registered users only Download (204kB)
	Text 19.K1.0033-TJIOE LOISE JUNIEVA KURNIADI-BAB V_a.pdf Restricted to Registered users only Download (81kB)
Preview	Text 19.K1.0033-TJIOE LOISE JUNIEVA KURNIADI-DAPUS_a.pdf Download (162kB) | Preview
	Text 19.K1.0033-TJIOE LOISE JUNIEVA KURNIADI-LAMP_a.pdf Restricted to Registered users only Download (223kB)

Abstract

Twitter is one of the most widely used and popular social media in Indonesia. The level of curiosity of the Indonesian people towards South Korea is very high, this is because South Korean culture has entered and provided new views, one of which is skincare products from South Korea, Cosrx. Skincare products from South Korea are something that Indonesians never stop talking about, especially on Twitter social media. This study aims to analyze the sentiment of Indonesian people towards South Korean skin care products, Cosrx. The data collection process carried out in this study uses the data scraping method on Twitter data. The algorithm used to perform sentiment analysis is the BiLSTM algorithm. The results obtained are the sentiment of Indonesian Twitter users giving a neutral impression of Cosrx skin care products from South Korea with an accuracy rate of 54.5% and F1-score results of 60.4%.

Item Type:	Thesis (Other)
Subjects:	000 Computer Science, Information and General Works > 004 Data processing & computer science
Divisions:	Faculty of Computer Science > Department of Informatics Engineering
Depositing User:	Mr Yosua Norman Rumondor
Date Deposited:	05 Oct 2023 06:42
Last Modified:	05 Oct 2023 06:42
URI:	http://repository.unika.ac.id/id/eprint/32968

Actions (login required)

View Item