scholarly journals Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan Covid-19 Menggunakan Data Twitter

2021 ◽  
Vol 5 (4) ◽  
pp. 820-826
Author(s):  
Yuyun ◽  
Nurul Hidayah ◽  
Supriadi Sahibu
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Weighted Average ◽  
Naïve Bayes ◽  
Public Sentiment ◽  
The Social ◽  
Bayes Algorithm ◽  
Special Approach ◽  
Class Labels

Currently, the spread of information Covid-19 is spreading rapidly. Not only through electronic media, but this information is also disseminated by user posts on social media. Due to the user text posted is varies greatly, it’s needs a special approach to classify these types of posts. This research aims to classify the public sentiment towards the handling of COVID-19. The data from this study were obtained from the social media application i.e., Twitter. This study uses a derivative of the Naïve Bayes algorithm, namely Multinomial Nave Bayes to optimize the classification results.  Three class labels are used to classify public sentiment namely positive, negative, and neutral sentiments. The stage starts with text preprocessing; cleaning, case folding, tokenization, filtering and stemming. Then proceed with weighting using the TF-IDF approach. To evaluate the classification results, data is tested using confusion matrix by testing accuracy, precision, and recall. From the test results, it is found that the weighted average for precision, recall and accuracy is 74%. Research shows that the accuracy of the proposed method has fair classification levels.

scholarly journals SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

2019 ◽  
Vol 15 (2) ◽  
pp. 247-254
Author(s):  
Heru Sukma Utama ◽  
Didi Rosiyadi ◽  
Dedi Aridarma ◽  
Bobby Suryo Prakoso
Keyword(s):  
Social Media ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Information Gain ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Support Vector ◽  
Toll Road ◽  
Textual Data ◽  
Bayes Algorithm

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

scholarly journals Application of Naïve Bayes Algorithm in Sentiment Analysis of Filipino, English and Taglish Facebook Comments

2020 ◽  
Vol 4 (5) ◽  
pp. 73-77
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Product Reviews ◽  
Documentary Data ◽  
The Social ◽  
Bayes Algorithm

The World Wide Web has boosted its content for the past years, it has a vast amount of multimedia resources that continuously grow specifically in documentary data. One of the major contributors of documentary contents can be evidently found on the social media called Facebook. People or netizens on Facebook are actively sharing their opinion about a certain topic or posts that can be related to them or not. With the huge amount of accessible documentary data that are seen on the so-called social media, there are research trends that can be made by the researchers in the field of opinion mining. A netizen’s comment on a particular post can either be a negative or a positive one. This study will discuss the opinion or comment of a netizen whether it is positive or negative or how she/he feels about a specific topic posted on Facebook; this is can be measured by the use of Sentiment Analysis. The combination of the Natural Language Processing and the analytics in textual form is also known as Sentiment Analysis that is use to the extraction of data in a useful manner. This study will be based on the product reviews of Filipinos in Filipino, English and Taglish (mixed Filipino and English) languages. To categorize a comment effectively, the Naïve Bayes Algorithm was implemented to the developed web system.

scholarly journals Determining Bullying Text Classification Using Naive Bayes Classification on Social Media

Jurnal Varian ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 133-140
Author(s):  
Ade Clinton Sitepu ◽  
Wanayumini Wanayumini ◽  
Zakarias Situmorang
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Rapid Development ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Naïve Bayes ◽  
Cyber Bullying ◽  
Training Data ◽  
The Media ◽  
Bayes Algorithm

Cyber-bullying includes repeated acts with the aim of scaring, angering, or embarrassing those who are targeted Cyber-bullying is happening along with the rapid development of technology and social media in society. The media and users need to filter out bully comments because they can indirectly affect the mental psychology that reads them especially directly aimed at that person. By utilizing information mining, the system is expected to be able to classify information circulating in the community. One of the classification techniques that can be applied to text-based classification is Naïve Bayes. The algorithm is good at performing the classification process. In this research, the precision of the algorithm's has been carried out on 1000 comment datasets. The data is grouped manually first into the labels "bully" and "not bully" then the data is divided into training data and test data. To test the system's ability, the classified data is analyzed using the confusion matrix method. The results showed that the Naïve Bayes Algorithm got the level of precision at 87%. and the level of  area under the curve (AUC) at 88%. In terms of speed of completing the system, the Naïve Bayes Algorithm has a very good rate of speed with completion time of 0.033 seconds.

scholarly journals Community Opinion Sentiment Analysis on Social Media Using Naive Bayes Algorithm Methods

2019 ◽  
Vol 2 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Taqwa Hariguna ◽  
Vera Rachmawati
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Central Java ◽  
Public Sentiment ◽  
Positive Class ◽  
Negative Class ◽  
Bayes Algorithm ◽  
F Measure

The election of Governor is an election event for the Regional Head for the future of the region and the country. The Central Java Governor election in 2018 was held jointly on 27 June 2018, which was followed by 2 candidate pairs of the governor. Its many responses from people through twitter's social media to bring up opinions from the public. Sentiment analysis of 2 research objects of Central Java Governor 2018 candidates with a total of 400 tweets with each candidate being 200 tweets. The used of tweets are divided into 3 classes: positive class, neutral class and negative class. In this study the classification process used the Naive Bayes Classifier (NBC) method, while for data preprocessing is using Cleansing, Punctuation Removal, Stopword Removal, and Tokenisation, to determine the sentiment class with the Lexicon Based method produces the highest accuracy in the Ganjar Pranowo dataset with an accuracy of 87,9545%, Precision value is 0.891%, Recall value is 0.88% and F-Measure is 0.851% while Sudirman Said dataset has an accuracy rate of 84.322%, Precision value of 0.867%, Recall value of 0.843% and F-Measure of 0.815%. From these results, we can conclude that the Ganjar Pranowo dataset was higher compared to Sudirman Said's dataset.

Analysis of Sentiment of Moving a National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine

2020 ◽  
Vol 4 (3) ◽  
pp. 504-512
Author(s):  
Faried Zamachsari ◽  
Gabriel Vangeran Saragih ◽  
Susafa'ati ◽  
Windu Gata
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Public Opinion ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Capital City ◽  
Support Vector ◽  
National Capital ◽  
Bayes Algorithm

The decision to move Indonesia's capital city to East Kalimantan received mixed responses on social media. When the poverty rate is still high and the country's finances are difficult to be a factor in disapproval of the relocation of the national capital. Twitter as one of the popular social media, is used by the public to express these opinions. How is the tendency of community responses related to the move of the National Capital and how to do public opinion sentiment analysis related to the move of the National Capital with Feature Selection Naive Bayes Algorithm and Support Vector Machine to get the highest accuracy value is the goal in this study. Sentiment analysis data will take from public opinion using Indonesian from Twitter social media tweets in a crawling manner. Search words used are #IbuKotaBaru and #PindahIbuKota. The stages of the research consisted of collecting data through social media Twitter, polarity, preprocessing consisting of the process of transform case, cleansing, tokenizing, filtering and stemming. The use of feature selection to increase the accuracy value will then enter the ratio that has been determined to be used by data testing and training. The next step is the comparison between the Support Vector Machine and Naive Bayes methods to determine which method is more accurate. In the data period above it was found 24.26% positive sentiment 75.74% negative sentiment related to the move of a new capital city. Accuracy results using Rapid Miner software, the best accuracy value of Naive Bayes with Feature Selection is at a ratio of 9:1 with an accuracy of 88.24% while the best accuracy results Support Vector Machine with Feature Selection is at a ratio of 5:5 with an accuracy of 78.77%.

The Sentiment Analysis Reviewing Indosat Services from Twitter Using the Naive Bayes Classifier

2020 ◽  
Vol 1 (2) ◽  
pp. 61-66
Author(s):  
Febri Astiko ◽  
Achmad Khodar
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Learning Model ◽  
Naïve Bayes ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Model ◽  
Bayes Algorithm

This study aims to design a machine learning model of sentiment analysis on Indosat Ooredoo service reviews on social media twitter using the Naive Bayes algorithm as a classifier of positive and negative labels. This sentiment analysis uses machine learning to get patterns an model that can be used again to predict new data.

scholarly journals Prediction of Solid Garbage Waste Generation in Smart Cities using Naive Bayes Algorithm

2019 ◽  
Vol 9 (2S) ◽  
pp. 53-56
Keyword(s):  
Naive Bayes ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Confusion Matrix ◽  
Daily Basis ◽  
Naïve Bayes ◽  
Human Beings ◽  
Waste Generation ◽  
Future Prediction ◽  
Bayes Algorithm

Smart cities which are becoming overcrowded today are making human beings life miserable and prone to more challenges on daily basis. Overcrowded is leading to vast generation of wastes contributing to air pollution and in turn is affecting health causing various diseases. Even though various measures are taken to recycle wastes, the rate at which it is being produced is becoming higher and higher. This paper deals with prediction of waste generation using Naïve Bayes machine learning algorithm(Classifier) based on the statistics of previous waste datasets. The datasets used for the future prediction are obtained from reliable sources. The implementation of the algorithm is done in Pyspark using Anaconda Jupyter. The performance of the classifier on the datasets is analyzed with confusion matrix and accuracy metric is used to rate the efficiency of the classifier. The accuracy obtained indicates that algorithm can be effectively used for real time prediction and it gives more accurate results for huge input datasets based on independence assumption.

scholarly journals Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease

2020 ◽  
Vol 4 (1) ◽  
pp. 76-85
Author(s):  
Dwi Yuni Utami ◽  
Elah Nurlelah ◽  
Noer Hikmah
Keyword(s):  
Genetic Algorithms ◽  
Liver Disease ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Attribute Selection ◽  
World Health ◽  
The Difference ◽  
Bayes Algorithm ◽  
Health Organization

Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.

scholarly journals Komparasi Algoritma Naive Bayes Dengan Algoritma Genetika Pada Analisis Sentimen Pengguna Busway

2019 ◽  
Vol 5 (2) ◽  
pp. 227-234
Author(s):  
Riska Aryanti ◽  
Atang Saepudin ◽  
Eka Fitriani ◽  
Rifky Permana ◽  
Dede Firmansyah Saefudin
Keyword(s):  
Genetic Algorithm ◽  
Social Media ◽  
Sentiment Analysis ◽  
Web Site ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Algorithm ◽  
The Social ◽  
The Will ◽  
Research Classification

Congestion major cities in Indonesi caused by the proliferation of the use of private vehicles. Some expressing he thinks about busway user through the social media and other web site, This opinion can be used as a sentiment analysis to see if the user busway proposes a review of positive or negative. The results of the analysis sentiment can help in the sight of and evaluate the use of busway, also expected to improve and transjakarta facility from so they tend to have an opinion positive. Based on the results of the analysis, sentiment it is hoped people will switch to using the will of course will reduce congestion. In the study also added the stages preprocesing by using the framework gataframework to complete the process that cannot be done on tools rapidminer. The methodology that was used in this research was it is anticipated that analysis the sentiment of the by the application of an genetic algorithm for an election features with an algorithm naive bayes. From the results of the testing to the case in research it is found that classification algorithm naive bayes based genetic algorithm having the kind of accuracy that good enough 88,55 % and value of auc reached 0,813 % with the level of the diagnosis classifications good. So that in this research classification algorithm naive bayes based genetic algorithm can be recommended as algorithms classifications good enough to analyze the busway user sentimen. Based on analysis is expected to private transport users will switch to using the busway will reduce congestion

scholarly journals ANALISIS SENTIMEN PADA PEMERINTAHAN TERPILIH PADA PILPRES 2019 DITWITTER MENGGUNAKAN ALGORITME NAÏVEBAYES

JURTEKSI ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 101-106
Author(s):  
Febby Apri Wenando ◽  
Regiolina Hayami ◽  
Agung Jefrianto Anggrawan
Keyword(s):  
Presidential Election ◽  
Naive Bayes ◽  
Vice President ◽  
Naïve Bayes ◽  
Weighting Method ◽  
The Social ◽  
Twitter Account ◽  
N Gram ◽  
Bayes Algorithm ◽  
Modeling Data

Abstract: The Presidential general election on 2019 became one of the most popular topics on twitter nowdays.  The society give their opinion about the  pair of  candidates that they are support through the social media. This research was predicts about the society sentimens toward the candidates of President and Vice President of Republic of Indonesia. The data was used  based on the tweet on the @jokowi twitter account. The retrieval of data by using the Tweepy library with the Python 2.7 programming language. This research was classified became of two of society sentiments classes, namely positive and negative. The modeling was used of the weighting method Unigram, Bigram, Trigram, N-Gram (1-2) and N-Gram (1-3)  that used the Naïve Bayes Algorithm on the Weka Application. The modeling data was used by the dataset of 646 sentences. The highest results  of this reseach were obtained  by Unigram Weighting, namely: 81.4% accuracy, 81.5% precision, 81.3% recall with a time of 0.3 s.Keywords: classification, naïve bayes, 2019 presidential election, twitter, unigram Abstrak: Pemilihan Umum tentang Pilpres 2019 menjadi salah satu topik yang ramai diperbincangkan di Twitter. Adu pendapat di sosial media oleh masyarakat mengandung opini terhadap pasangan calon yang didukungnya. Penelitian ini memprediksi sentimen masyarakat kepada pasangan calon Presiden dan Wakil Presiden Republik Indonesia. Data yang digunakan adalah tweet yang ada pada akun Twitter @jokowi. Pengambilan data menggunakan library Tweepy dengan bahasa pemrograman Python 2.7. Penelitian ini mengklasifikasi sentimen masyarakat menjadi 2 kelas, yaitu positif dan negatif. Kemudian dilakukan pemodelan dengan metode pembobotan Unigram, Bigram, Trigram, N-Gram (1-2) Dan N-Gram (1-3) menggunakan Algoritme Naïve Bayes pada Aplikasi Weka. Pembuatan model menggunakan dataset yang berjumlah 646 kalimat. Hasil tertinggi yang diperoleh pada penelitian ini adalah dengan menggunakan Pembobotan Unigram, yaitu : akurasi 81,4%, presisi 81,5 % , recall 81,3 % dengan catatan waktu 0,3s.Kata kunci: klasifikasi, naïve bayes, pilpres 2019, twitter, unigram.

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