scholarly journals Sentiment Analysis of Social Media Twitter with Case of Anti-LGBT Campaign in Indonesia using Naïve Bayes, Decision Tree, and Random Forest Algorithm

2019 ◽  
Vol 161 ◽  
pp. 765-772 ◽  
Author(s):  
Veny Amilia Fitri ◽  
Rachmadita Andreswari ◽  
Muhammad Azani Hasibuan
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Random Forest Algorithm

scholarly journals Tweet Netizen Prediction Using Random Forest, Decision Tree, Naïve Bayes, And Ensemble Algorithm (Case Study The Governor Of DKI Jakarta)

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 9-20
Author(s):  
Antonius Yadi Kuntoro
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Testing Data ◽  
Negative Comments ◽  
Ensemble Algorithm ◽  
Bayes Algorithm

Abstract — The current Governor of DKI Jakarta, even though he has been elected since 2017 is always interesting to talk about or even comment on. Comments that appear come from the media directly or through social media. Twitter has become one of the social media that is often used as a media to comment on elected governors and can even become a trending topic on Twitter social media. Netizens who comment are also varied, some are always Tweeting criticism, some are commenting Positively, and some are only re-Tweeting. In this research, a prediction of whether active Netizens will tend to always lead to Positive or Negative comments will be carried out in this study. Model algorithms used are Decision Tree, Naïve Bayes, Random Forest, and also Ensemble. Twitter data that is processed must go through preprocessing first before proceeding using Rapidminer. In trials using Rapidminer conducted in four trials by dividing into two parts, namely testing data and training data. Comparisons made are 10% testing data: 90% Training data, then 20% testing data: 80% training data, then 30% testing data: 70% training data, and the last is 35% testing data: 65% training data. The average Accuracy for the Decision Tree algorithm is 93.15%, while for the Naïve Bayes algorithm the Accuracy is 91.55%, then for the Random Forest algorithm is 93.41, and the last is the Ensemble algorithm with an Accuracy of 93, 42%. here. Keywords — Decision Tree, Naïve Bayes, Random Forest, Set, Twitter.  

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 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 Komparasi Tujuh Algoritma Identifikasi Fraud ATM Pada PT. Bank Central Asia Tbk

2020 ◽  
Vol 7 (3) ◽  
pp. 441-450
Author(s):  
Haliem Sunata
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Decision Tree ◽  
Central Asia ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Random Tree

Tingginya penggunaan mesin ATM, sehingga menimbulkan celah fraud yang dapat dilakukan oleh pihak ketiga dalam membantu PT. Bank Central Asia Tbk untuk menjaga mesin ATM agar selalu siap digunakan oleh nasabah. Lambat dan sulitnya mengidentifikasi fraud mesin ATM menjadi salah satu kendala yang dihadapi PT. Bank Central Asia Tbk. Dengan adanya permasalahan tersebut maka peneliti mengumpulkan 5 dataset dan melakukan pre-processing dataset sehingga dapat digunakan untuk pemodelan dan pengujian algoritma, guna menjawab permasalahan yang terjadi. Dilakukan 7 perbandingan algoritma diantaranya decision tree, gradient boosted trees, logistic regression, naive bayes ( kernel ), naive bayes, random forest dan random tree. Setelah dilakukan pemodelan dan pengujian didapatkan hasil bahwa algoritma gradient boosted trees merupakan algoritma terbaik dengan hasil akurasi sebesar 99.85% dan nilai AUC sebesar 1, tingginya hasil algoritma ini disebabkan karena kecocokan setiap attribut yang diuji dengan karakter gradient boosted trees dimana algoritma ini menyimpan dan mengevaluasi hasil yang ada. Maka algoritma gradient boosted trees merupakan penyelesaian dari permasalahan yang dihadapi oleh PT. Bank Central Asia Tbk.

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.

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