scholarly journals Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS

2021 ◽  
Vol 5 (4) ◽  
pp. 646
Author(s):  
Rani Puspita ◽  
Agus Widodo
Keyword(s):  
Decision Tree ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Good Service ◽  
Precision Class ◽  
Bayes Methods ◽  
Twitter Data ◽  
Decision Tree Method ◽  
Tree Method

BPJS is really helpful because one of its goal is to provide good service for the member in terms of healthiness. But, when there’s many people using the service, then it will cause more pros and contras. Therefore, researcher will be doing sentiment analysis in the field of data mining towards bpjs users on social media Twitter as much as 1000 data that later will be filtered to be 903 data because there are some data that has been duplicated. Researchers used the KNN, Decision Tree, and Naïve Bayes methods to compare the accuracy of the three methods. Researchers used the RapidMiner version 9.7.2 tools. The results showed that the sentiment analysis of Twitter data on BPJS services using the KNN method reached an accuracy level of 95.58% with class precision for pred. negative is 45.00%, pred. positive is 0.00%, and pred. neutral is 96.83%. Then the Decision Tree method the accuracy rate reaches 96.13% with the precision class for pred. negative is 55.00%, pred. positive is 0.00%, and pred. neutral is 97.28%. And the last one is the Naïve Bayes method which achieves 89.14% accuracy with precision class for pred. negative is 16.67%, pred. positive was 1.64%, and pred. neutral is 98.40%.

scholarly journals Sentiment Analysis of Online Transportation Service using the Naïve Bayes Methods

2020 ◽  
Vol 1641 ◽  
pp. 012093
Author(s):  
M Tika Adilah ◽  
Hendra Supendar ◽  
Rahayu Ningsih ◽  
Sri Muryani ◽  
Kusmayanti Solecha
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Bayes Methods ◽  
Transportation Service

Sentiment Analysis of Tweets on the COVID-19 Pandemic Using Machine Learning Techniques

2021 ◽  
pp. 310-320
Author(s):  
Jothikumar R. ◽  
Vijay Anand R. ◽  
Visu P. ◽  
Kumar R. ◽  
Susi S. ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Respiratory Tract ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Machine Learning Techniques ◽  
Respiratory Tract Diseases ◽  
Thought Processes ◽  
Learning Techniques

Sentiment evaluation alludes to separate the sentiments from the characteristic language and to perceive the mentality about the exact theme. Novel corona infection, a harmful malady ailment, is spreading out of the blue through the quarter, which thought processes respiratory tract diseases that can change from gentle to extraordinary levels. Because of its quick nature of spreading and no conceived cure, it ushered in a vibe of stress and pressure. In this chapter, a framework perusing principally based procedure is utilized to discover the musings of the tweets related to COVID and its effect lockdown. The chapter examines the tweets identified with the hash tags of crown infection and lockdown. The tweets were marked fabulous, negative, or fair, and a posting of classifiers has been utilized to investigate the precision and execution. The classifiers utilized have been under the four models which incorporate decision tree, regression, helpful asset vector framework, and naïve Bayes forms.

scholarly journals Sentiment Analysis of Perpustakaan Nasional Republik Indonesia Through Social Media Twitter

MATICS ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 90
Author(s):  
Fakhris Khusnu Reza Mahfud
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Classification Performance ◽  
Naïve Bayes ◽  
User Needs ◽  
Twitter Data ◽  
Knn Classification ◽  
National Libraries

The library is a gate of science and a heart of civilization. Indonesia already has a Perpustakaan Nasional consisted of 27 floors and is equipped with facilities that are adequate for user needs. Apart from that, we need to see opinions from the community as users. Public opinion about the library is critical for library managers to evaluate services and facilities from the library. One way to find out the views of the community is by using social media twitter. Twitter social media is often used in channelling opinions or expressing opinions about specific topics; besides social media, twitter is commonly used for digital campaign movements. Submission of views and even digital campaigns on Twitter social media greatly influence the opinions and even behaviour of society in various ways. This study analyzes tweets about national libraries by classifying, positive opinions, negative opinions and neutral opinions. In this study, twitter data will go through the preprocessing, weighting, and classification stages. TF-IDF and TF binary are used in weighting in this study. The classification used in this study is Naive Bayes and KNN. Accuracy, precision, and recall values were also used in this study to evaluate classification performance. The highest classification performance using KNN classification with TF-IDF weighting resulted in the value of accuracy, precision, and recall of 83.33%, 79.2%, and 83.3% respectively.

Sentiment Analysis of Snack Review Using the Naïve Bayes Method

2020 ◽  
Vol 8 (3) ◽  
pp. 333
Author(s):  
I Gede Cahya Purnama Yasa ◽  
Ngurah Agus Sanjaya ER ◽  
Luh Arida Ayu Rahning Putri
Keyword(s):  
Sentiment Analysis ◽  
Fast Food ◽  
Naive Bayes ◽  
Food Products ◽  
Naïve Bayes ◽  
Level Of Service ◽  
Snack Food ◽  
Bayes Method ◽  
Bayes Methods

Fast food is a product that we often encounter in stores such as convenience stores. Ready-to-eat products can now be easily found by consumers. One of the reason is due to the expansion of minimarkets in areas that are easily reached, such as housing complexes, school areas, and offices. Sentiment analysis is used to determine whether an opinion or comment on a product has a positive or negative interest and can be used as a reference in improving service, or improving product quality. In this research, we study the sentiments of consumers towards snack food products as a reference to improve the level of service and quality of these products.. We classify the sentiment of a review on snack food products as positive and negative. To classify the sentiments we apply the Naïve Bayes and Multinomial Naïve Bayes methods. We compare the two methods to study the most effective and efficient method for classifying sentiments on reviews of snack food products. Keywords: Sentiment Analysis, TF-IDF, Naïve Bayes,Multinomial, Review, Snack, Preprocessing

Sentiment Analysis of Twitter Data Using Naïve Bayes Classifier

2021 ◽  
pp. 1227-1234
Author(s):  
S. R. Shankara Gowda ◽  
B. R. Archana ◽  
Praajna Shettigar ◽  
Kislay Kumar Satyarthi
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Twitter Data
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