Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Information Technology (BIT)
Siti Setiawati
Universitas Bahayangkara Jaya
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
A Implementation of Text Mining In Sentiment Analysis of Shopee Indonesia Using SVM Kusdarnowo Hantoro; Dwipa Handayani; Siti Setiawati
Bulletin of Information Technology (BIT) Vol 3 No 2: Juni 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v3i2.282

Abstract

Many online shopping users convey an assessment of a product through status comments. Comments indicate a non-standard form of expression. Nowadays, user comments are increasing rapidly, which makes data management difficult. Today's society has so many choices; most of them are used for preference as a final recommendation. The top item shows the preferences of the available items, recommended based on future predictability. The predictive rating of some items is used by the user to recommend the item to other users. Sentiment analysis can recommend items of choice for online shopping in various fields of application, especially in e-commerce. Sentiment analysis is used to identify opinions, ideas, or thoughts from online media. Shopee is one of the largest online business platforms. Classification Sentiment analysis towards Shopee based on the Support Vector Machine (SVM) was used in this study on the Shopee application using 990 training data and 110 test data. From the test data, 28 data entered the negative class and the remaining 82 data entered the positive class and resulted in an accuracy rate of 80.90%, meaning that from 110 assessments there were 89 assessments classified exactly in the sentiment class.
Co-Authors Dwipa Handayani Hantoro, Kusdarnowo