ANALISIS SENTIMEN TERHADAP ULASAN KEPUASAN PELANGGAN PADA MARKETPLACE TOKOPEDIA DI JEJARING SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES

Muhammad Abi Nurhakim, . (2021) ANALISIS SENTIMEN TERHADAP ULASAN KEPUASAN PELANGGAN PADA MARKETPLACE TOKOPEDIA DI JEJARING SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAIVE BAYES. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

[img] Text
ABSTRAK.pdf
Download (189kB)
[img] Text
AWAL .pdf
Download (657kB)
[img] Text
BAB I.pdf
Download (138kB)
[img] Text
BAB II.pdf
Restricted to Repository UPNVJ Only
Download (506kB)
[img] Text
BAB III.pdf
Restricted to Repository UPNVJ Only
Download (714kB)
[img] Text
BAB IV.pdf
Restricted to Repository UPNVJ Only
Download (1MB)
[img] Text
BAB V.pdf
Download (300kB)
[img] Text
DAFTAR PUSTAKA.pdf
Download (411kB)
[img] Text
RIWAYAT HIDUP.pdf
Restricted to Repository UPNVJ Only
Download (201kB)
[img] Text
LAMPIRAN.pdf
Restricted to Repository UPNVJ Only
Download (969kB)
[img] Text
HASIL PLAGIARISME.pdf
Restricted to Repository staff only
Download (9MB)
[img] Text
ARTIKEL KI.pdf
Restricted to Repository staff only
Download (456kB)

Abstract

Consumer reviews about shopping satisfaction in the marketplace are valuable information and can be processed properly. This information can be used to obtain product and service evaluations by both consumers and sellers or marketplaces by analyzing the shopping experience reviews through the Twitter social network in order to obtain the necessary information. Review analysis activities cannot be enough just to look at the number of ratings, but it is necessary to look at the entire content of the review to be able to find out the broader and general meaning of the review. If a small amount can be done manually, but to see a large number of reviews, a system is needed to be able to analyze more effectively and make it easier to understand the purpose of the review. In this study using the Naïve Bayes classification method which is divided into 2 classes, namely positive and negative. The results of the classification using the Naïve Bayes algorithm obtained an accuracy of 79.02%, precision of 80.30%, recall of 77.94% and specificity of 80.15%.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 1710511083] [Pembimbing 1: Yuni Widiastiwi] [Pembimbing 2: Nurul Chamidah] [Penguji 1: Didit Widiyanto] [Penguji 2: Iin Ernawati]
Uncontrolled Keywords: Twitter, Reviews, Classification, Naive Bayes
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Muhammad Abi Nurhakim
Date Deposited: 07 Mar 2022 02:30
Last Modified: 07 Mar 2022 02:30
URI: http://repository.upnvj.ac.id/id/eprint/16166

Actions (login required)

View Item View Item