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 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.

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 Sentiment Analysis Using Naive Bayes Algorithm with Feature Selection Particle Swarm Optimization (PSO) and Genetic Algorithm

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Abi Rafdi ◽  
Herman Mawengkang Herman ◽  
Syahril Efendi
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Particle Swarm ◽  
Naïve Bayes ◽  
Swarm Optimization ◽  
Bayes Algorithm

This study analyzes Sentiment to see opinions, points of view, judgments, attitudes, and emotions towards creatures and aspects expressed through texts. One of Social Media is like Twitter is one of the most widely used means of communication as a research topic. The main problem with sentiment analysis is voting and using the best feature options for maximum results. Either, the most widely known classification method is Naive Bayes. However, Naive Bayes is very sensitive to significant features. That way, in this test, a comparison of feature selection is carried out using Particle Swarm Optimization and Genetic Algorithm to improve the accuracy performance of the Naive Bayes algorithm. Analyses are performed by comparing before and after testing using feature selection. Validation uses a cross-validation technique, while the confusion matrix ??is appealed to measure accuracy. The results showed the highest increase for Naïve Bayes algorithm accuracy when using the feature selection of the Particle Swarm Optimization Algorithm from 60.26% to 77.50%, while the genetic algorithm from 60.26% to 70.71%. Therefore, the choice of the best characteristics is Particle Swarm Optimization which is superior with an increase in accuracy of 17.24%.

scholarly journals THE IMPLEMENTATION OF THE MACHINE LEARNING ALGORITHM FOR THE SENTIMENT ANALYSIS OF INDONESIA’S 2019 PRESIDENTIAL ELECTION

2021 ◽  
Vol 22 (1) ◽  
pp. 78-92
Author(s):  
GA Buntoro ◽  
R Arifin ◽  
GN Syaifuddiin ◽  
A Selamat ◽  
O Krejcar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Naïve Bayes ◽  
Machine Learning Algorithm ◽  
Presidential Candidates ◽  
N Gram

In 2019, citizens of Indonesia participated in the democratic process of electing a new president, vice president, and various legislative candidates for the country. The 2019 Indonesian presidential election was very tense in terms of the candidates' campaigns in cyberspace, especially on social media sites such as Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, etc. The Indonesian people used social media platforms to express their positive, neutral, and also negative opinions on the respective presidential candidates. The campaigning of respective social media users on their choice of candidates for regents, governors, and legislative positions up to presidential candidates was conducted via the Internet and online media. Therefore, the aim of this paper is to conduct sentiment analysis on the candidates in the 2019 Indonesia presidential election based on Twitter datasets. The study used datasets on the opinions expressed by the Indonesian people available on Twitter with the hashtags (#) containing "Jokowi and Prabowo." We conducted data pre-processing using a selection of comments, data cleansing, text parsing, sentence normalization and tokenization based on the given text in the Indonesian language, determination of class attributes, and, finally, we classified the Twitter posts with the hashtags (#) using Naïve Bayes Classifier (NBC) and a Support Vector Machine (SVM) to achieve an optimal and maximum optimization accuracy. The study provides benefits in terms of helping the community to research opinions on Twitter that contain positive, neutral, or negative sentiments. Sentiment Analysis on the candidates in the 2019 Indonesian presidential election on Twitter using non-conventional processes resulted in cost, time, and effort savings. This research proved that the combination of the SVM machine learning algorithm and alphabetic tokenization produced the highest accuracy value of 79.02%. While the lowest accuracy value in this study was obtained with a combination of the NBC machine learning algorithm and N-gram tokenization with an accuracy value of 44.94%. ABSTRAK: Pada tahun 2019 rakyat Indonesia telah terlibat dalam proses demokrasi memilih presiden baru, wakil presiden, dan berbagai calon legislatif negara. Pemilihan presiden Indonesia 2019 sangat tegang dalam kempen calon di ruang siber, terutama di laman media sosial seperti Facebook, Twitter, Instagram, Google+, Tumblr, LinkedIn, dll. Rakyat Indonesia menggunakan platfom media sosial bagi menyatakan pendapat positif, berkecuali, dan juga negatif terhadap calon presiden masing-masing. Kampen pencalonan menteri, gabenor, dan perundangan hingga pencalonan presiden dilakukan melalui media internet dan atas talian. Oleh itu, kajian ini dilakukan bagi menilai sentimen terhadap calon pemilihan presiden Indonesia 2019 berdasarkan kumpulan data Twitter. Kajian ini menggunakan kumpulan data yang diungkapkan oleh rakyat Indonesia yang terdapat di Twitter dengan hashtag (#) yang mengandungi "Jokowi dan Prabowo." Proses data dibuat menggunakan pilihan komentar, pembersihan data, penguraian teks, normalisasi kalimat, dan tokenisasi teks dalam bahasa Indonesia, penentuan atribut kelas, dan akhirnya, pengklasifikasian catatan Twitter dengan hashtag (#) menggunakan Klasifikasi Naïve Bayes (NBC) dan Mesin Vektor Sokongan (SVM) bagi mencapai ketepatan optimum dan maksimum. Kajian ini memberikan faedah dari segi membantu masyarakat meneliti pendapat di Twitter yang mengandungi sentimen positif, neutral, atau negatif. Analisis Sentimen terhadap calon dalam pemilihan presiden Indonesia 2019 di Twitter menggunakan proses bukan konvensional menghasilkan penjimatan kos, waktu, dan usaha. Penyelidikan ini membuktikan bahawa gabungan algoritma pembelajaran mesin SVM dan tokenisasi abjad menghasilkan nilai ketepatan tertinggi iaitu 79.02%. Manakala nilai ketepatan terendah dalam kajian ini diperoleh dengan kombinasi algoritma pembelajaran mesin NBC dan tokenisasi N-gram dengan nilai ketepatan 44.94%.

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