ANALISIS SENTIMENT PELANGGAN TERHADAP PENILAIAN PRODUK PADA TOKO ONLINE SHOP AMRETA MENGGUNAKAN METODE NAIVE BAYES CLASSIFICATION

STONE, ALISIA SILVER and Fathoni, Fathoni (2022) ANALISIS SENTIMENT PELANGGAN TERHADAP PENILAIAN PRODUK PADA TOKO ONLINE SHOP AMRETA MENGGUNAKAN METODE NAIVE BAYES CLASSIFICATION. Undergraduate thesis, Sriwijaya University.

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Abstract

Sentiment analysis or opinion mining is an analysis that aims to see the sentiment of people or groups regarding certain entities. The sentiments expressed by society can be in positive, negative and neutral form. One media that can be given an opinion by the public is in the e-commerce application, namely the shopee application, shopee has a comment or assessment feature on the product that has been purchased. Toko which was used as a sample of researchersan is an amreta online shop store , based on the results of the identification of the problem, it was found that the fact was reviewed that many comments that did not match the stars were given, so it can be assumed that the rating cannot represent that the performance of the store is good. Amreta is already good but gives little star value or vice versa. Therefore, the author conducted sentiment analysis research on amreta stores to assist the store in providing references when they were going to conduct an evaluation. Thenext one is processed using rapidminer tools while for operators in the form of algorithms using the Sentiment Naïve Bayes Classification model through automatic calculations. The results of the study can be concluded that the test data obtained have an accuracy level of 97.15% using the Naive Bayes Classification model. Keywords: Sentiment, Online Shop, Shopee, Naïve Bayes, E-Commerce

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sentiment, Online Shop, Shopee, Naïve Bayes, E-Commerce
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Alisia Silver Stone
Date Deposited:	08 Sep 2022 05:59
Last Modified:	08 Sep 2022 05:59
URI:	http://repository.unsri.ac.id/id/eprint/78540

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