scholarly journals INTERNET NETWORK CLASSIFICATION IN MALIKUSSALEH UNIVERSITY USING NAÏVE BAYES METHOD

2021 ◽  
Vol 5 (1) ◽  
pp. 48-53
Author(s):  
Eva Darnila ◽  
◽  
Zara Yunizar ◽  
Dhyra Gibran Alinda
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Browsing ◽  
Bayes Method ◽  
Test Information ◽  
The World ◽  
The Web ◽  
Web Systems ◽  
Internet Network

The utilize of web systems at this time is exceptionally critical, particularly for the world of instruction. In expansion to the significance of the web arrange, issues frequently emerge on the web arrange due to an expansive number of clients, the issues can be gotten at the UNIMAL campus incorporate moderate, harmed, and indeed not sent information to its goal since the organize activity isn't ideal. To be able to optimize the web organize by prioritizing organize activity. In this consider, the Credulous Bayes calculation is utilized for the classification handle of organized activity capture information. The application utilized to capture organize activity is the Wireshark application. Utilizing the Credulous Bayes calculation to watch the comes about of organizing test information through a calculation prepare that has tall precision. To be specific at the Workforce of Designing 95.73% with a likelihood of testing comes about is 0.00015946 web browsing, 0.00000007 downloads, 0.00008691 gushing, 0.00008497 social media, 0.00000014 floodings.

scholarly journals Communication Sentiment Analyzer using Machine Learning with Naive Bayes Bernoullinb

2019 ◽  
Vol 9 (1) ◽  
pp. 5976-5979
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Major Part ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
User Preferences ◽  
Social Media Data ◽  
Machine Learning Model ◽  
The World ◽  
Media Data

In this never-ending social media era it is estimated that over 5 billion people use smartphones. Out of these, there are over 1.5 billion active users in the world. In which we all are a major part and before opening our messages we all are curious about what message we have received. No doubt, we all always hope for a good message to be received. So Sentiment analysis on social media data has been seen by many as an effective tool to monitor user preferences and inclination. Finally, we propose a scalable machine learning model to analyze the polarity of a communicative text using Naive Bayes’ Bernoulli classifier. This paper works on only two polarities that is whether the sentence is positive or negative. Bernoulli classifier is used in this paper because it is best suited for binary inputs which in turn enhances the accuracy of up to 97%.

scholarly journals Sistem Pakar Diagnosa Virus Corona Dengan Metode Naïve Bayes

2021 ◽  
Vol 9 (1) ◽  
pp. 81
Author(s):  
Fareza Aditiyanto Nugroho ◽  
Arif Fajar Solikin ◽  
Mutiara Dwi Anggraini ◽  
Kusrini Kusrini
Keyword(s):  
Expert System ◽  
Natural Disaster ◽  
Naive Bayes ◽  
Virus Disease ◽  
Diagnostic System ◽  
Naïve Bayes ◽  
The Body ◽  
Human Being ◽  
Bayes Method ◽  
The World

Humans being are faced with non-natural disasters which have bad effect for population on the world. This non-natural disaster is called Corona Virus Disease (COVID-19). This COVID-19 will become a pandemic in 2020. This types of COVID-19 is coming from the Orthocronavirinae. It belongs to the Coronaviridae and the Nidovirales. This type of that virus has caused some disease to birds, mammals and also human being. Therefore, the research was conducted. The result of this research will give the information about system which related the classification human being according to their transmission to the body. This research used naïve bayes method. The result of this research is diagnostic system with the level of accuracy 94%. Thus, COVID-19 diagnostic expert system used to know the level of COVID -19 infections to human being. It can help the user knowing the next treatment.Keywords : Expert System, Naïve Bayes, Coronavirus, Covid-19

scholarly journals Analisis Sentimen dan Pemodelan Topik Pariwisata Lombok Menggunakan Algoritma Naive Bayes dan Latent Dirichlet Allocation

2021 ◽  
Vol 5 (1) ◽  
pp. 123-131
Author(s):  
Ni Luh Putu Merawati Putu ◽  
Ahmad Zuli Amrullah ◽  
Ismarmiaty
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Bayes Method ◽  
A Value ◽  
Negative Class ◽  
Naive Bayes Method ◽  
Dirichlet Allocation

Lombok Island is one of the favorite tourist destinations. Various topics and comments about Lombok tourism experience through social media accounts are difficult to manually identify public sentiments and topics. The opinion expressed by tourists through social media is interesting for further research. This study aims to classify tourists' opinions into two classes, positive and negative, and topics modelling by using the Naive Bayes method and modeling the topic by using Latent Dirichlet Allocation (LDA). The stages of this research include data collection, data cleaning, data transformation, data classification. The results performance testing of the classification model using Naive Bayes method is shown with an accuracy value of 92%, precision of 100%, recall of 84% and specificity of 100%. The results of modeling topics using LDA in each positive and negative class from the coherence value shows the highest value for the positive class was obtained on the 8th topic with a value of 0.613 and for the negative class on the 12th topic with a value of 0.528. The use of the Naive Bayes and LDA algorithms is considered effective for analyzing the sentiment and topic modelling for Lombok tourism.  

scholarly journals TEXT MINING DALAM MEMBANDINGKAN METODE NAÏVE BAYES DENGAN C.45 DALAM MENGIDENTIFIKASI BERITA HOAX PADA MEDIA SOSIAL

2022 ◽  
Vol 5 (1) ◽  
pp. 116-123
Author(s):  
Yola Tri Handika ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Detection System ◽  
High Accuracy ◽  
Naïve Bayes ◽  
Major Influence ◽  
Bayes Method ◽  
Training Stage ◽  
Political Conditions ◽  
Naive Bayes Method

Hoax news (hocus to trick) has a very big influence in disseminating information, especially in the world of social media. News has an important impact on social and political conditions, and news can move the economy of a country. For this reason, it is necessary to have an analysis to classify hoax news and not hoaxes, and have high accuracy in classifying the news. In this study, two methods were used as a comparison in achieving high accuracy, namely the Naïve Bayes method which is famous for having high accuracy in classification with little data, and the C.45 method which can minimize noise in the data. The data used are 300 articles with 10 topics which contain hoax and non-hoax news. The data is obtained from the internet through social media, such as Twitter, Instagram and Facebook. Testing using the Naïve Bayes method has a higher accuracy than the C.45 method. The amount of data used has a major influence on the test results, if more data enters the training stage, then this study will have higher accuracy. However, the results of this test can be recommended to increase accuracy in the construction of a hoax news detection system.

Sentiment Analysis of the Enforcement of PSBB Part II in Jakarta

2020 ◽  
Vol 9 (2) ◽  
pp. 259
Author(s):  
Gede Putra Aditya Brahmantha ◽  
I Wayan Santiyasa
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Web Crawling ◽  
K Nearest Neighbor ◽  
Bayes Method ◽  
The Public ◽  
Naive Bayes Method

In addition to communicating, Social Media is a place to issue opinions by the public on many things that are currently taking place, Twitter is one of these social medias that is widely used in conveying opinions regardless of whether these opinions are negative, positive, or even neutral. Tweets data about the Enforcement of PSBB Part II in Jakarta were obtained as many as 200 opinions using web crawling then advanced to the preprocessing stage before being classified using the K-Nearest Neighbor and Multinomial Naive Bayes algorithms. In 3 tests, the highest accuracy was 65.00% for K-Nearest Neighbor and the highest accuracy was 85.00% for Multinomial Naive Bayes method.

scholarly journals Implementasi Sistem Pakar untuk Mendeteksi Virus Covid-19 dengan Perbandingan Metode Naïve Bayes dan Certainty Factor

2021 ◽  
Vol 5 (3) ◽  
pp. 338
Author(s):  
Rio Al Dzahabi Yunas ◽  
Agung Triayudi ◽  
Ira Diana Sholihati
Keyword(s):  
Naive Bayes ◽  
Medical Personnel ◽  
Naïve Bayes ◽  
Certainty Factor ◽  
Bayes Method ◽  
Web Based ◽  
Factor Method ◽  
Technological Advances ◽  
The World ◽  
Naive Bayes Method

The Covid -19 virus spread in the world, especially in Indonesia, very fast. This epidemic is of concern around the world because it has a quite bad impact in various sectors. With existing technological advances, the Expert System can assist medical personnel in detecting the Covid -19 Virus. The purpose of the author in conducting the study was to detect the Covid-19 virus as easily as possible with symptom data obtained from patients who had consultations. The Naïve Bayes method is a method that uses probability and statistics that can predict a person's chance of being exposed to Covid-19 in the future based on symptoms experienced in the previous period packed with a web-based program. For comparison, the author uses the Certainty Factor Method. Certainty Factor is a method that aims to determine the certainty value which is based on the previous calculation of CF value by manual calculation. The Naïve Bayes method can group the symptoms obtained from the official WHO website which has been given an indicator of the percentage of someone exposed to the Covid-19 Virus based on the symptom data experienced to determine a person exposed to the Covid-19 Virus. While the Certainty Factor method gets the confidence of someone exposed to the symptoms of the Covid-19 virus by using the calculation indicator on the CF value that has been consulted by the user, which can provide a percentage level of confidence that is 86%.Keywords:Expert System, Covid-19, Naive Bayes, Certainty Factor.

scholarly journals Sentiment Evaluation of Public Transport in Social Media using Naïve Bayes Method

2019 ◽  
Vol 9 (1) ◽  
pp. 2305-2308 ◽  
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Business Organization ◽  
Bayes Method ◽  
Transportation Services ◽  
Business Values ◽  
Company Reputation ◽  
Machine Learning Approach ◽  
Naive Bayes Method

Nowadays, there is a trend in business organization to use social media as a medium to get feedback from customers. This gives advantage in improving the business values such as increasing customers’ satisfactions and building better company reputation. However, the response and feedback from the customers are varies and hold different perspectives. It might be led to ambiguous answer.In this work, we utilized Naïve Bayes machine learning approach for analyzing sentiment at social media on transportation services. We collected all feedback from Facebook and Twitter about transportation services. From the unstructured comments and feedback, we classified accordingly to determine the related scope of the sentiment. By using the Naïve Bayes method those massivecomments and feedback are presented in appropriate way and easier to understand.

scholarly journals Sistem Pakar Dalam Diagnosa Penyakit Kanker Rahim Menggunakan Metode Naïve Bayes dan Certainty Factor

2021 ◽  
Vol 5 (1) ◽  
pp. 251
Author(s):  
Yendrizal Yendrizal
Keyword(s):  
Health Care ◽  
Cervical Cancer ◽  
Naive Bayes ◽  
Uterine Cancer ◽  
Reproductive Organs ◽  
Naïve Bayes ◽  
Uterine Disease ◽  
Certainty Factor ◽  
Bayes Method ◽  
The World

The uterus is one of the reproductive organs, namely the mouth of the uterus which is very susceptible to cancer and very often women experience cancer due to a lack of health care. suffered by women in the uterus can be like cysts, cervical cancer, uterine cancer, vaginal cancer and others, cancer is also very difficult to cure so that patients eventually have to give up and face death. From this it is necessary to make a diagnosis from the beginning in order to minimize the number of deaths caused by cervical cancer which is faced by many women in the world, especially in Indonesia, in diagnosing this disease can use a computer system in collaboration with experts to produce a system called the system. experts as an effort to help solve problems that occur with uterine disease that is being experienced by some women. The results of this study will show the percentage level of disease experienced by patients by 88% with the help of implementing the naïve Bayes method and certainty factory

scholarly journals Analisis Sentimen Pada Media Sosial Twitter Menggunakan Naive Bayes Classifier Dengan Ekstrasi Fitur N-Gram

2018 ◽  
Vol 2 (2) ◽  
pp. 200
Author(s):  
Agung Nugroho
Keyword(s):  
Social Media ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Bayes Method ◽  
N Gram ◽  
Naive Bayes Method

Social media is currently an online media that is widely accessed in the world. Microblogging services such as Twitter allow users to write about various things they experience or write reviews of a product, service, public figures and so on. This can be used to take opinion or sentiment towards an entity that is being discussed on social media such as Twitter. This study utilizes these data to determine public opinion or sentiment regarding public perceptions of the issue of rising electricity tariffs. Opinion taking is based on three classes namely positive, negative and neutral. Users often use non-standard word abbreviations or spelling, this can complicate the process and accuracy of classification results. In this study the authors apply text-preprocessing in handling these problems. For feature extraction, n-gram and classification methods are used using the Naive Bayes classifier. From the results of the research that has been done, the most negative sentiments are formed in response to the issue of the increase in basic electricity tariffs. In addition, from the results of testing with the method of cross validation and confusion matrix it is known that the accuracy of the naïve Bayes method reaches 89.67% before applying n-gram, and the accuracy rate increases 2.33% after applying n-gram characters to 92.00%. It is proven that the application of the n-gram extraction feature can increase the accuracy of the naïve Bayes method.

scholarly journals PENERAPAN ALGORITMA NAIVE BAYES DAN PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI BERITA HOAX PADA MEDIA SOSIAL

2020 ◽  
Vol 5 (2) ◽  
pp. 159-164
Author(s):  
Risa Wati
Keyword(s):  
Social Media ◽  
Particle Swarm Optimization ◽  
Naive Bayes ◽  
Particle Swarm ◽  
Optimization Method ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Swarm Optimization ◽  
Conducting Research ◽  
Naive Bayes Method

Social media is the most effective way to facilitate fast information, unfortunately, there are some elements who use social media to add hoax or deception to give misleading opinions to the public. Therefore a method is needed to classify hoax news and non-hoax news on social media. Naive Bayes is a simple classification algorithm but has high qualifications, but Naive Bayes has a very sensitive shortcoming in the selection of features and therefore the Particle Swarm Optimization method is needed to improve the expected results. After conducting research with the Naive Bayes method and the Naive Bayes method based on Particle Swarm Optimization, the results obtained are Naive Bayes yielding 74.67% while the Naive Bayes based on Particle Swarm Optimization with an accuracy value of 85.19%. The purpose of this study is to see a large comparison. Swarm Optimization particles to improve accuracy in the classification of hoax news on social media using the Naive Bayes classifier. After using Particle Swarm Optimization the test results increased by 10.52%.

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