scholarly journals Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 43-51
Author(s):  
Hendrik Setiawan ◽  
Ema Utami ◽  
Sudarmawan Sudarmawan
Keyword(s):  
Support Vector Machine ◽  
World Health ◽  
Support Vector ◽  
Online Lectures ◽  
Svm Algorithm ◽  
The World ◽  
The Government ◽  
Bayes Algorithm ◽  
Health Organization ◽  
Performance Results

The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of "at home" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm  

scholarly journals Sentimen Analisis Terkait Lockdown pada Sosial Media Twitter

2020 ◽  
Vol 6 (2) ◽  
pp. 223-229
Author(s):  
Muhammad Dwison Alizah ◽  
Arifin Nugroho ◽  
Ummu Radiyah ◽  
Windu Gata
Keyword(s):  
Support Vector Machine ◽  
World Health Organization ◽  
Predictive Modeling ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
Support Vector ◽  
The World ◽  
Negative Impacts ◽  
Health Organization

Abstract:  Covid-19 has been set as a Pandemic by the World Health Organization (WHO). The very large impact and the infection that is fast enough are the reasons for making Covid-19 as a pandemic and efforts to overcome. One anticipation that can be done is to do lockdown. Making the decision to carry out a lockdown is intended to reduce the spread that occurs. Lockdown is certainly not a 100% good solution for all of individual. There are individual who agree that the lockdown will be implemented, also there are those who think that the lockdown is better not to be carried out considering the negative impacts that can occur. Therefore in this study will be presented the predictive modeling for sentiment analysis related to "lockdown" specially on social media Twitter. The method used to labeled was using Vader then the tweets are extracted using TF-IDF, and modeling is made for the prediction of sentiment using Naïve Bayes and Support Vector Machine. The results obtained from the two algorithms are more than 80%. Keywords: Covid-19, lockdown, TF-IDF, Naïve Bayes, Support Vector Machine Abstrak: Covid-19 telah ditetapkan sebagia Pandemi oleh World Health Organization (WHO). Dampak yang sangat besar dan penyebaran yang cukup cepat menjadi alsan untuk menjadikan Covid-19 sebagai Pandemi dan perlu dilakukan upaya penanggulangan. Salah satu upaya yang bisa dilakukan adalah dengan melakukan lockdown. Pengambilan keputusan untuk melakukan lockdown diperuntukan guna mengurangi penyebaran yang terjadi. Lockdown tentunya bukanlah solusi yang 100% baik bagi segala pihak. Terdapat pihak - pihak yang menyetujui akan dilaksanakannya lockdown, ada pula yang beranggapan bahwa lockdown lebih baik tidak dilaksanakan dengan pertimbangan dampak negatif yang bisa terjadi. Oleh karena itu, pada penelitian ini akan disampaikan mengenai pembuatan pemodelan prediksi terkait analisa sentimen terkait “Lockdown” yang dikhususkan pada media sosial Twitter. Metode yang digunakan adalah dengan melakukan labeling menggunakan Vader dan selanjutnya tweet tersebut dilakukan ekstraksi menggunakan TF-IDF, dan dibuatkan pemodelan untuk prediksi sentimen menggunakan Naïve Bayes dan Support Vector Machine. Hasil evaluasi yang didapat dari kedua algoritma tersebut ialah mencapai lebih dari 80%. Kata kunci: Covid-19, lockdown, TF-IDF, Naïve Bayes, Support Vector Machine Abstract:  Covid-19 has been set as a Pandemic by the World Health Organization (WHO). The very large impact and the infection that is fast enough are the reasons for making Covid-19 as a pandemic and efforts to overcome. One anticipation that can be done is to do lockdown. Making the decision to carry out a lockdown is intended to reduce the spread that occurs. Lockdown is certainly not a 100% good solution for all of individual. There are individual who agree that the lockdown will be implemented, also there are those who think that the lockdown is better not to be carried out considering the negative impacts that can occur. Therefore in this study will be presented the predictive modeling for sentiment analysis related to "lockdown" specially on social media Twitter. The method used to labeled was using Vader then the tweets are extracted using TF-IDF, and modeling is made for the prediction of sentiment using Naïve Bayes and Support Vector Machine. The results obtained from the two algorithms are more than 80%. Keywords:Covid-19, lockdown, TF-IDF, Naïve Bayes, Support Vector Machine Abstrak: Covid-19 telah ditetapkan sebagia Pandemi oleh World Health Organization (WHO). Dampak yang sangat besar dan penyebaran yang cukup cepat menjadi alsan untuk menjadikan Covid-19 sebagai Pandemi dan perlu dilakukan upaya penanggulangan. Salah satu upaya yang bisa dilakukan adalah dengan melakukan lockdown. Pengambilan keputusan untuk melakukan lockdown diperuntukan guna mengurangi penyebaran yang terjadi. Lockdown tentunya bukanlah solusi yang 100% baik bagi segala pihak. Terdapat pihak - pihak yang menyetujui akan dilaksanakannya lockdown, ada pula yang beranggapan bahwa lockdown lebih baik tidak dilaksanakan dengan pertimbangan dampak negatif yang bisa terjadi. Oleh karena itu, pada penelitian ini akan disampaikan mengenai pembuatan pemodelan prediksi terkait analisa sentimen terkait “Lockdown” yang dikhususkan pada media sosial Twitter. Metode yang digunakan adalah dengan melakukan labeling menggunakan Vader dan selanjutnya tweet tersebut dilakukan ekstraksi menggunakan TF-IDF, dan dibuatkan pemodelan untuk prediksi sentimen menggunakan Naïve Bayes dan Support Vector Machine. Hasil evaluasi yang didapat dari kedua algoritma tersebut ialah mencapai lebih dari 80%. Kata kunci: Covid-19, lockdown, TF-IDF, Naïve Bayes, Support Vector Machine

scholarly journals Analisis Sentimen Opini Terhadap Vaksin Covid - 19 pada Media Sosial Twitter Menggunakan Support Vector Machine dan Naive Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 19-25
Author(s):  
Frizka Fitriana ◽  
Ema Utami ◽  
Hanif Al Fatta
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
World Health ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Bayes Algorithm ◽  
Health Organization ◽  
The Right ◽  
The Impact

The corona virus outbreak, commonly referred to as COVID-19, has been officially designated a global pandemic by the World Health Organization (WHO). To minimize the impact caused by the virus, one of the right steps is to develop a vaccine, however, with the vaccination for the Indonesian people, it is controversial so that it invites many people to give an opinion assessment, but the limited space makes it difficult for the public to express their opinion, because Therefore, people choose social media as a place to channel public opinion. Support vector machine algorithm has better performance in terms of accuracy, precision and recall with values ​​of 90.47%, 90.23%, 90.78% with performance values ​​on the Bayes algorithm, namely 88.64%, 87.32%, 88, 13%, with a difference of 1.83% accuracy, 2.91% precision and 2.65% recall, while for time the Naive Bayes algorithm has a better performance level with a value of 8.1 seconds and the Support vector machine algorithm gets a time speed of 11 seconds with a difference of 2, 9 seconds. With the results of sentiment analysis neutral 8.76%, negative 42.92% and positive 48.32% for Bayes and neutral 10.56%, negative 41.28% and positive 48.16% for SVM.

Algorithm Comparation of Naive Bayes and Support Vector Machine based on Particle Swarm Optimization in Sentiment Analysis of Freight Forwarding Services

2020 ◽  
Vol 4 (2) ◽  
pp. 362-369
Author(s):  
Sharazita Dyah Anggita ◽  
Ikmah
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
The Public ◽  
Svm Algorithm ◽  
Bayes Algorithm ◽  
Freight Forwarding ◽  
Improved Accuracy

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

ARIMA model for forecasting COVID-19 Death cases in India (Preprint)

2020 ◽  
Author(s):  
Jeya Sutha M
Keyword(s):  
Arima Model ◽  
Public Health Emergency ◽  
World Health ◽  
The People ◽  
The World ◽  
The Status ◽  
The Government ◽  
Health Organization ◽  
Spread Of Infection ◽  
Death Cases

UNSTRUCTURED COVID-19, the disease caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a highly contagious disease. On January 30, 2020 the World Health Organization declared the outbreak as a Public Health Emergency of International Concern. As of July 25, 2020; 15,947,292 laboratory-confirmed and 642,814 deaths have been reported globally. India has reported 1,338,928 confirmed cases and 31,412 deaths till date. This paper presents different aspects of COVID-19, visualization of the spread of infection and presents the ARIMA model for forecasting the status of COVID-19 death cases in the next 50 days in order to take necessary precaution by the Government to save the people.

scholarly journals A PIECEWISE GROWTH MODEL FOR MODELING THE ACCUMULATED NUMBER OF COVID-19 CASES IN THE CITY OF CAMPO GRANDE

2021 ◽  
Vol 39 (1) ◽  
pp. 240
Author(s):  
Erlandson Ferreira SARAIVA ◽  
Leandro SAUER ◽  
Basílio De Bragança PEREIRA ◽  
Carlos Alberto de Bragança PEREIRA
Keyword(s):  
Growth Model ◽  
Health Department ◽  
World Health ◽  
Mathematical Study ◽  
Number Of Patients ◽  
Technical Report ◽  
The World ◽  
The Government ◽  
Health Organization ◽  
The City

In December of 2019, a new coronavirus was discovered in the city of Wuhan, China. The World Health Organization officially named this coronavirus as COVID-19. Since its discovery, the virus has spread rapidly around the world and is currently one of the main health problems, causing an enormous social and economic burden. Due to this, there is a great interest in mathematical models capable of projecting the evolution of the disease in countries, states and/or cities. This interest is mainly due to the fact that the projections may help the government agents in making decisions in relation to the prevention of the disease. By using this argument, the health department of the city (HDC) of Campo Grande asked the UFMS for the development of a mathematical study to project the evolution of the disease in the city. In this paper, we describe a modeling procedure used to fit a piecewise growth model for the accumulated number of cases recorded in the city. From the fitted model, we estimate the date in which the pandemic peak is reached and project the number of patients who will need treatment in intensive care units. Weekly, was sent to HDC a technical report describing the main results.

scholarly journals Penggunaan Aplikasi Google Classroom sebagai Metode Pembelajaran Daring Selama Pandemi Covid-19

2021 ◽  
Vol 14 (2) ◽  
pp. 11-17
Author(s):  
Mirza Ghulamudin Ghulamudin ◽  
Maufur ◽  
Beni Habibi
Keyword(s):  
Distance Learning ◽  
World Health ◽  
Ministry Of Education ◽  
Health Crisis ◽  
Use Of Technology ◽  
The World ◽  
Teachers And Students ◽  
The Government ◽  
Health Organization ◽  
In The Beginning

Covid-19 has now attacked Indonesia, where the spread of the disease is very fast. Not only in Indonesia, but all corners of the world are currently experiencing a health crisis. In the beginning, the spread of Covid-19 had an impact on economic activity which began to sluggish. This also has an impact on the education system in Indonesia. Until several countries decided to close schools and universities. In an effort to prevent the spread of covid-19, the World Health Organization (WHO) recommends temporarily stopping activities that would potentially cause crowds. Even during the outbreak, covid-19 in Indonesia, there were many ways that the government did to prevent its spread through social distancing. Kemendikbud instructed through the Ministry of Education and Culture (Kemendikbud) Directorate of Higher Education Circular No. 1 of 2020 concerning the prevention of the spread of covid-19 in the world of Education to organize distance learning and advise students to learn from their homes. Teachers and students are starting to be required to follow the current situation by using technology as a distance learning medium. One of the media that is being favored by teachers as a learning medium is the Google Classroom application. This application is an application that can make it easier for students and teachers to create effective learning. Given that students today are a generation who are very familiar with the use of technology. The use of technology in learning is an alternative method used by teachers during the Covid-19 Pandemic.

scholarly journals Strategies for prevention and control of COVID-19 in Nepal

2020 ◽  
Vol 7 (1) ◽  
pp. 85-88 ◽  
Author(s):  
Kiran Sapkota ◽  
Ganesh Dangal ◽  
Madhu Koirala ◽  
Kalyan Sapkota ◽  
Asmita Poudel ◽  
...  
Keyword(s):  
World Health Organization ◽  
Prevention And Control ◽  
Preventive Measures ◽  
Large Population ◽  
World Health ◽  
The World ◽  
Community Transmission ◽  
The Government ◽  
Health Organization ◽  
And Control

Coronavirus disease (COVID-19) outbreak, caused by the most recently discovered coronavirus, is currently affecting a large population across the globe. World health organization (WHO) has already declared COVID-19, a pandemic, and the world is fighting to contain the COVID-19 outbreak. Nepal has taken several preventive measures to control the coronavirus outbreak. However, some additional steps are needed to prevent community transmission of the disease. This brief communication discusses the government of Nepal actions and provides recommendations for the prevention and control of COVID-19 infection in Nepal.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals The Homeless-Poor and the COVID-19 Stay-at-Home Policy of Government: Rethinking the Plight of Homelessness in Nigeria

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110450
Author(s):  
Cletus O. Obasi ◽  
Christopher M. Anierobi
Keyword(s):  
Homeless People ◽  
World Health ◽  
Urban Centers ◽  
Healthcare Facilities ◽  
The World ◽  
Negative Perceptions ◽  
Policy Measures ◽  
The Government ◽  
Health Organization ◽  
At Home

The Government Stay-at-Home policy regarding the COVID-19 pandemic has seemingly aggravated the plight of the homeless, especially in Nigeria that has about 108 million homeless people who require special attention. Observations show that most of them situate randomly across urban centers in Nigeria and have no access to pronounced shelter, healthcare facilities, hygiene materials, and defined source of income. In response to the global COVID-19 pandemic currently ravaging many countries, most governments adopted the Stay-at-Home policy among some other policy measures that were recommended by the World Health Organization toward containing the spread of the virus. Given the COVID-19 Stay-at-Home policy of Nigerian Government, this study looked into the plight of the homeless-poor with a view to evolve an inclusive pro-poor housing strategy for the country. Findings show that the homeless poor have negative perceptions of the Stay-at-Home policy, which they see as being rather inimical to their survival. The study highlights lack of space and shelter as major reasons for their aversion to social distancing. Their reasons have serious implications for compliance to the COVID-19 Stay-at-Home policy as well as the spread curve of the virus. The study therefore recommends a review of the COVID-19 Regulation toward incorporating “Pro-Poor Housing Provisions” measures strictly for the homeless poor in the country. Strong collaboration between government and established institutions, such as Government-Church/Mosque strategy, is also recommended.

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