Analisis Sentimen Masyarakat Terhadap Virus Corona Berdasarkan Opini Masyarakat Menggunakan Metode Naïve Bayes Classifier

The current use of social media is not only to communicate between friends, but is often also used as a means to convey an aspiration to the community, especially the Indonesian people regarding government issues, or problems related to health and other problems. One of the uses of this social media is to use it as a means of conveying digital aspirations, such as some slogans that are used as hashtags, namely #dirumahaja #lockdown, #usemasker, #protocol, #imun, #vaccine. From the slogan used as a hashtag, researchers are interested in conducting research on how much negative sentiment and positive sentiment there are, using the Naïve Bayes Classifier method, which is a machine learning method that uses probability calculations. The basic concept used by Nave Bayes is the Bayes Classifier Theorem, which is a theorem in statistics to calculate probability, the Bayes Optimal Classifier calculates the probability of one class from each existing attribute group, and determines which class is the most optimal, as for the advantages of using Nave Bayes Classifier in document classification can be viewed from the process that takes action based on existing data to provide solutions to these sentiments.

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Analisis Sentimen Masyarakat Berdasarkan Opini dari Sosial Media Menggunakan Metode Naive Bayes Classifier (Study Kasus : Universitas Sjakhyakirti)

Jurnal Ilmiah Informatika Global ◽

10.36982/jig.v11i1.1071 ◽

2020 ◽

Vol 11 (1) ◽

Author(s):

Paisal Paisal

Keyword(s):

Machine Learning ◽

Data Mining ◽

Social Media ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Mining Operations ◽

Use Of Social Media

AbstractThe use of social media today is not only to communicate between friends, but also is needed to make facilities to convey the aspirations of certain people in Indonesia about legal issues relating to government and other issues. One of the aspirations conveyed through social media is a hash that is widely seen by one of the Sjakhyakirti University from the use of social media. Then there arises a lot of sentiment from every community, there are those that give positive sentiments and also negative sentiments that can have a good or bad impact on daily life. days in the community. Some reasons for positive and negative sentiments sourced from this social media, will use social media. From this debate the researchers found a solution where this hashtag can provide good results for the general public or vice versa. In analyzing this, the researcher uses the Naïve Bayes Classifier method which is one of the machine learning methods that uses calculations, the classification of automated hashes can help minimize personal misclassification by obtaining positive or negative sentiment information by using data mining that is carried out by using tools that execute the tools that execute data mining operations that have been determined based on the analysis of models of hidden data on big data thus outlining the discovery of knowledge about Sjakhyakirti University.Keywords : Social Media, Sjakhyakirti, Naïve Bayes ClassifieAbstrakPemanfaatan sosial media saat ini tidak hanya untuk berkomunikasi antara teman saja, akan tetapi sering juga dijadikan sebuah sarana untuk menyampaikan suatu aspirasi bagi masyarakat khususnya masyarakat indonesia mengenai masalah hukum ataupun masalah yang berhubungan dengan pemerintahan serta masalah lainnnya. Salah satu aspirasi yang disampaikan melalui sosial media ini adalah sebuah hastag yang banyak dilihat setiap harinya salah satunya mengenai Universitas Sjakhyakirti dari pemanfaaat sosial media ini maka munculah banyak sentimen dari setiap masyarakat, ada yang memberikan sentimen positif dan juga sentimen negatif mengenai tanggapan terhadap hastag tersebut yang dapat berdampak baik atau buruk bagi kehidupan sehari-hari dimasyarakat. Beberapa alasan sentimen positif dan negatif yang bersumber dari sosial media ini, akan memanfaatkan sosial media. Dari permasalahan ini peneliti menghasilkan sebuah solusi dimana hastag tersebut apakah dapat memberikan dampak yang baik bagi masyarakat umumumnya ataupun sebaliknya. Dalam menganalisa ini, peneliti menggunakan metode Naïve Bayes Classifier yang merupakan salah satu metode machine learning yang menggunakan perhitungan probabilitas, pengklasifikasian hastag otomatis ini dapat disesuaikan sehingga meminimalisasi aksi salah pengklasifikasian secara personal dengan memproleh informasi sentimen positif atau negative dengan menggunakan data mining yang dilakukan dengan tool weka yang mengeksekusi operasi data mining yang telah didefinisikan berdasarkan model analisis dari data tersembunyi pada sejumlah data besar sehingga menguraikan penemuan pengetahuan mengenai Universitas Sjakhyakirti.Kata kunci : Sosial Media, Sjakhyakirti, Naïve Bayes Classifie

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Analisis Sentiment Tweets Berbahasa Sunda Menggunakan Naive Bayes Classifier dengan Seleksi Feature Chi Squared Statistic

Jurnal Informatika Universitas Pamulang ◽

10.32493/informatika.v4i3.3186 ◽

2019 ◽

Vol 4 (3) ◽

pp. 87

Author(s):

Yono Cahyono ◽

Saprudin Saprudin

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Chi Square ◽

Chi Squared ◽

Use Of Social Media

At present the development of the use of social media in Indonesia is very rapid, in Indonesia there are a variety of regional languages, one of which is the Sundanese language, where some people especially those living in West Java use Sundanese language to express comments, opinions, suggestions, criticisms and others in social media. This information can be used as valuable data for individuals or organizations in decision making. The huge amount of data makes it impossible for humans to read and analyze it manually. Sentiment analysis is the process of classifying opinions, analyzing, understanding, evaluating, emotions and attitudes towards a particular entity such as individuals, organizations, products or services, topics, events, in order to obtain information. The purpose of this research is the Naїve Bayes Classifier (NBC) classification algorithm and Feature Chi Squared Statistics selection method can be used in Sundanese-language tweets sentiment analysis on Twitter social media into positive, negative and neutral categories. Chi Square Statistic feature test results can reduce irrelevant features in the Naïve Bayes Classifier classification process on Sundanese-language tweets with an accuracy of 78.48%.

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Sentiment Analysis Of Online Lecture Opinions On Twitter Social Media Using Naive Bayes Classifier

10.1109/icomitee53461.2021.9650135 ◽

2021 ◽

Author(s):

Devi Ajeng Damaratih

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Online Lecture

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

CAUCHY ◽

10.18860/ca.v7i1.12488 ◽

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 ◽

Mining Social Media Data of Philippine Higher Education Institutions Using Naive Bayes Classifier Algorithm

SSRN Electronic Journal ◽

10.2139/ssrn.3379025 ◽

2019 ◽

Author(s):

Joey Aviles ◽

Rosanna Esquivel

Keyword(s):

Higher Education ◽

Social Media ◽

Naive Bayes ◽

Higher Education Institutions ◽

Naïve Bayes ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Social Media Data ◽

Media Data

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Knowing Personality Traits on Facebook Status Using the Naïve Bayes Classifier

International Journal of Artificial Intelligence & Robotics (IJAIR) ◽

10.25139/ijair.v2i1.2636 ◽

2020 ◽

Vol 2 (1) ◽

pp. 22

Author(s):

Mohammad Zoqi Sarwani ◽

Muhammad Shubkhan Salafudin ◽

Dian Ahkam Sani

Keyword(s):

Social Media ◽

Big Five ◽

Naive Bayes ◽

Naïve Bayes ◽

Training Data ◽

Naive Bayes Classifier ◽

Bayes Classifier ◽

Naïve Bayes Classifier ◽

Surrounding Environment ◽

Testing Data

With the development of social media trends among students by using Facebook social media, students can communicate and pour out everything that is felt in the form of status. Personality is the character or various characters of a person - therefore, how a person to adjust to the surrounding environment for the achievement of communication smoothly. In the personality category, many things classify a person's category in the psychologist theory. In this exercise, the Big Five, the psychologist theory, is described in five codes, namely Openness, Conscientiousness, Extraversion, Agreeables, Neuroticism. Naive Bayes Classifier is used to determine the highest probability value with the aim to determine the highest value. The data used are two namely training data and testing data obtained from the Facebook status of students. From the data obtained can be tested in the system that the accuracy value is 88%.

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