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All Journal Elkom: Jurnal Elektronika dan Komputer
T.D. Wismarini
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Analisis Sentimen Pada Ulasan Produk Dengan Metode Natural Language Processing (NLP) Rizal Chandra Rivaldi; T.D. Wismarini
Elkom : Jurnal Elektronika dan Komputer Vol 17 No 1 (2024): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v17i1.1680

Abstract

n today's digital era, customer reviews play a crucial role in purchasing decisions, but the large volume of reviews makes manual analysis difficult. Thus, a fast and accurate sentiment analysis method using Natural Language Processing (NLP) is needed. This research aims to analyze product reviews for the ZALIKA STORE 88 on Shopee using NLP. It involves preprocessing reviews, applying NLP techniques like tokenization, stemming, and lexical analysis, and automatically classifying sentiments. The analysis of ZALIKA STORE 88's reviews reveals mostly positive sentiments, with some negative and neutral reviews. The sentiment analysis achieved an 87% accuracy rate. This research is intended to help ZALIKA STORE 88 make informed decisions based on customer reviews.
Analisis Sentimen Pada Ulasan Produk Dengan Metode Natural Language Processing (NLP) : (Studi Kasus Zalika Store 88 Shopee) Rizal Chandra Rivaldi; T.D. Wismarini
Elkom: Jurnal Elektronika dan Komputer Vol. 17 No. 1 (2024): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v17i1.1680

Abstract

n today's digital era, customer reviews play a crucial role in purchasing decisions, but the large volume of reviews makes manual analysis difficult. Thus, a fast and accurate sentiment analysis method using Natural Language Processing (NLP) is needed. This research aims to analyze product reviews for the ZALIKA STORE 88 on Shopee using NLP. It involves preprocessing reviews, applying NLP techniques like tokenization, stemming, and lexical analysis, and automatically classifying sentiments. The analysis of ZALIKA STORE 88's reviews reveals mostly positive sentiments, with some negative and neutral reviews. The sentiment analysis achieved an 87% accuracy rate. This research is intended to help ZALIKA STORE 88 make informed decisions based on customer reviews.
Co-Authors Rizal Chandra Rivaldi