scholarly journals Analisis Sentimen Masyarakat Terhadap Pilpres 2019 Berdasarkan Opini Dari Twitter Menggunakan Metode Naive Bayes Classifier

2020 ◽  
Vol 1 (3) ◽  
pp. 185-199
Author(s):  
Khoirul Zuhri ◽  
Nurul Adha Oktarini Saputri
Keyword(s):  
Sentiment Analysis ◽  
Hate Speech ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
The Public ◽  
Positive Sentiment

Twitter is a social media that is currently popular, where the public is free to comment and write anything. It is not uncommon for the public to comment with harsh words and even hate speech. The 2019 presidential election drew many comments, some praised, criticized and insulted. To be able to dig up information and classify a text, sentiment analysis is needed. In this study, sentiment analysis is a process of classifying textual documents into two classes, namely negative and positive sentiment classes. Opinion data were obtained from the Twitter social network in the form of tweets. The data used was 3337 tweets consisting of 80% training data and 20% training data. Training data is data with known sentiment. This study aims to determine whether a tweet is a positive or negative tweet conveyed on Twitter in Indonesian. The classification of tweet data uses the naïve Bayes classifier algorithm. The classification results of the test data show that the Naïve Bayes Classifier algorithm provides an accuracy value of 71%. The accuracy value for each sentiment is 71% for positive sentiment and 70% for negative sentiment

scholarly journals COMPARISON OF NAIVE BAYES ALGORITHM AND C.45 ALGORITHM IN CLASSIFICATION OF POOR COMMUNITIES RECEIVING NON CASH FOOD ASSISTANCE IN WANASARI VILLAGE KARAWANG REGENCY

2020 ◽  
Vol 17 (1) ◽  
pp. 37-42
Author(s):  
Yuris Alkhalifi ◽  
Ainun Zumarniansyah ◽  
Rian Ardianto ◽  
Nila Hardi ◽  
Annisa Elfina Augustia
Keyword(s):  
Decision Tree ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Total Sample ◽  
Naïve Bayes ◽  
Food Assistance ◽  
Training Data ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier

Non-Cash Food Assistance or Bantuan Pangan Non-Tunai (BPNT) is food assistance from the government given to the Beneficiary Family (KPM) every month through an electronic account mechanism that is used only to buy food at the Electronic Shop Mutual Assistance Joint Business Group Hope Family Program (e-Warong KUBE PKH ) or food traders working with Bank Himbara. In its distribution, BPNT still has problems that occur that are experienced by the village apparatus especially the apparatus of Desa Wanasari on making decisions, which ones are worthy of receiving (poor) and not worthy of receiving (not poor). So one way that helps in making decisions can be done through the concept of data mining. In this study, a comparison of 2 algorithms will be carried out namely Naive Bayes Classifier and Decision Tree C.45. The total sample used is as much as 200 head of household data which will then be divided into 2 parts into validation techniques is 90% training data and 10% test data of the total sample used then the proposed model is made in the RapidMiner application and then evaluated using the Confusion Matrix table to find out the highest level of accuracy from 2 of these methods. The results in this classification indicate that the level of accuracy in the Naive Bayes Classifier method is 98.89% and the accuracy level in the Decision Tree C.45 method is 95.00%. Then the conclusion that in this study the algorithm with the highest level of accuracy is the Naive Bayes Classifier algorithm method with a difference in the accuracy rate of 3.89%.

scholarly journals Sentiment Analysis on Government Performance in Tourism During The COVID-19 Pandemic Period With Lexicon Based

CAUCHY ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 28-39
Author(s):  
Adri Priadana ◽  
Ahmad Ashril Rizal
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tourism Industry ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Trending Topics ◽  
The Government ◽  
Negative Sentiment

The COVID-19 pandemic impact has affected all industries in Indonesia and even the world, including the tourism industry. Researchers have a role in researching to answer the needs of the tourism industry, especially in making tourism and business destination management programs and carrying out activities oriented to meet the needs of the tourism industry. Meanwhile, the government has a role in making policies, especially in the roadmap, for developing the tourism industry. This study aims to track trending topics in social media Instagram since COVID-19 hit. The results of trending topics will be classified by sentiment analysis using a Lexicon-based and Naive Bayes Classifier. Based on Instagram data taken since January 2020, it shows the five highest topics in the tourism sector, namely health protocols, hotels, homes, streets, and beaches. Of the five topics, sentiment analysis was carried out with the Lexicon-based and Naive Bayes classifier, showing that beaches get an incredibly positive sentiment, namely 80.87%, and hotels provide the highest negative sentiment 57.89%. The accuracy of the Confusion matrix's sentiment results shows that the accuracy, precision, and recall are 82.53%, 86.99%, and 83.43%, respectively.

Indonesian Twitter Sentiment Analysis Application on The Covid l9 Vaccine Using Naive Bayes Classifier

2021 ◽  
Author(s):  
Adhitia Erfina ◽  
Moneyta Dholah Rosita Ndk ◽  
Rahmat Hidayat ◽  
Aris Subagja ◽  
Haerul Ramadhan ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Analysis Application
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