scholarly journals Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes

2021 ◽  
Vol 5 (1) ◽  
pp. 157
Author(s):  
Samsir Samsir ◽  
Ambiyar Ambiyar ◽  
Unung Verawardina ◽  
Firman Edi ◽  
Ronal Watrianthos
Keyword(s):  
Online Learning ◽  
High Frequency ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Face To Face ◽  
Face To Face Learning ◽  
Bayes Algorithm ◽  
Sudden Transition ◽  
Negative Sentiment

The WHO announced that more than 52 million people tested positive for Covid-19, and 1.2 million died in the second week of November 2020. Meanwhile, Indonesia recorded 463 thousand individuals with 15,148 deaths that were confirmed positive. Strategy against pandemics by incorporating socialization. However, learning that was initially bold as a technique became controversial due to the briefness of the adaptation process. a wide continuum of social reactions has resulted in the sudden transition from face-to-face learning to bold learning on a large scale. This research focuses on public opinion on online learning during the Indonesian COVID-19 pandemic in early November 2020. The analysis was carried out on Twitter by mining document-based text that was interpreted using the Naïve Bayes algorithm. The results show that online learning has a positive sentiment of 30 percent, a negative sentiment of 69 percent, and a neutral 1 percent over the period. Due to community dissatisfaction about online learning, a significant amount of negative sentiment is created. Some tweets indicate disappointment with the words' stress 'and' lazy 'in the conversation being high-frequency words.

scholarly journals Implementasi Algoritma Naive Bayes Untuk Memprediksi Predikat Ketuntasan Belajar Siswa Pasca Pandemi Covid 19

2021 ◽  
Vol 4 (1) ◽  
pp. 29-38
Author(s):  
Muhammad Saiful ◽  
◽  
Samsuddin Samsuddin ◽  
Keyword(s):  
Online Learning ◽  
Probabilistic Reasoning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Learning System ◽  
Learning Activities ◽  
Student Knowledge ◽  
Face To Face ◽  
Bayes Algorithm ◽  
The Right

During the Covid-19 pandemic, SMA Negeri 3 Selong changed learning activities from what was originally face-to-face, but currently learning is being transferred to the Online Learning System (SPADA) using several existing platforms. Judging from the level of plurality of students' thinking patterns during the implementation of online learning there are many problems that arise, one of which is the instability of the internet network, the various hendpone media devices owned by students and the lack of student knowledge in using online platforms. The purpose of this study was to determine the indicator of the problem in the predicate of student learning completeness of class XII SMAN 3 Selong during the post-COVID-19 pandemic. The method used to solve this problem is the Naïve Bayes algorithm. Naive Bayes is a method of probabilistic reasoning. And in the future the results of this study are expected to be able to provide the right solution in solving problems in online learning.

scholarly journals EFEKTIVITAS PEMBELAJARAN ONLINE SAAT PANDEMI COVID-19 DI UNIVERSITAS ISLAM NEGERI AR-RANIRY BANDA ACEH

2022 ◽  
Vol 7 (1) ◽  
pp. 13-26
Author(s):  
Muazzinah ◽  
Akhiar
Keyword(s):  
Online Learning ◽  
Large Scale ◽  
Daily Routine ◽  
Research Subjects ◽  
Public Places ◽  
Policy Makers ◽  
Face To Face ◽  
Public Facilities ◽  
Face To Face Learning ◽  
Banda Aceh

The Covid-19 pandemic has changed the daily routine of work. Since the issuance of PP Number 21 of 2020 concerning Large-Scale Social Restrictions, activities in public places or public facilities have been limited with the aim of reducing the spread of Covid-19. Likewise with the learning process, the Covid-19 pandemic has changed the conventional face-to-face learning pattern to face-to-face online. Therefore, this study aims to determine how effective online learning is at UIN Ar-Raniry Banda Aceh. The research method used in this study is a qualitative method with a descriptive approach with observation, interviews and documentation techniques. The research subjects are LPM UIN Ar-Raniry as policy makers as well as lecturers and students at UIN Ar-Raniry as implementers of online learning. The results of this study indicate that online learning at UIN Ar-Raniry Banda Aceh has not been effective, but to achieve this effectiveness the campus has tried as much as possible by doing several things, namely before the implementation of online learning activities the campus first designed planning and survey conditions and training to lecturers, then formulate policies, formulate goals and strategies, determine facilities and supervision which is carried out routinely through the Siakad portal. Keywords: Effectiveness, Online Learning, Covid-19 Pandemic

scholarly journals Public Perceptions of Online Learning in Developing Countries: A Study Using The ELK Stack for Sentiment Analysis on Twitter

2020 ◽  
Vol 15 (09) ◽  
pp. 94
Author(s):  
Satria Fadil Persada ◽  
Andri Oktavianto ◽  
Bobby Miraja ◽  
Reny Nadlifatin ◽  
Prawira Fajarindra Belgiawan ◽  
...  
Keyword(s):  
Developing Countries ◽  
Online Learning ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Developed Countries ◽  
Naïve Bayes ◽  
Public Perceptions ◽  
Traditional Research ◽  
Bayes Algorithm ◽  
Python Programming

This study explores public perceptions toward online learning application in Indonesia. Many studies about online learning were done in developed countries and only a few in developing countries. Moreover, these studies used a qualitative approach which limits the results to be applied in different settings. While traditional research using a survey to understand people's perception towards an entity requires a lot of time and efforts; we used efficient and effective manners to gather opinions and then analysed its sentiments using the Logstash, Kibana and Python programming language stack (ELK) stack and Naïve Bayes algorithm. We used Naïve Bayes algorithm for sentiment analysis and ELK stack for storing & gathering tweets from Twitter. With ELK stack, we successfully collected 133.477 tweets related to online learning. From this study, we understood what kind of words that are sentimentally positive and negative tweets. We also gained some insights regarding Indonesia’s student online learning application preferences.

scholarly journals Doc2Vec &Naïve Bayes: Learners’ Cognitive Presence Assessment through Asynchronous Online Discussion TQ Transcripts

2019 ◽  
Vol 14 (08) ◽  
pp. 70 ◽  
Author(s):  
Hind Hayati ◽  
Abdessamad Chanaa ◽  
Mohammed Khalidi Idrissi ◽  
Samir Bennani
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Online Discussions ◽  
Cognitive Presence ◽  
Machine Learning Techniques ◽  
Face To Face ◽  
Learning Techniques ◽  
Bayes Algorithm ◽  
Context Features

Due to the lack of face to face interaction in online learning environment, this article aims essentially to give tutors the opportunity to understand and analyze learners’ cognitive behavior. In this perspective, we propose an automatic system to assess learners’ cognitive presence regarding their social interactions within synchronous online discussions. Combining Natural Language Preprocessing, Doc2Vec document embedding method and machine learning techniques; we first make some transformations and preprocessing to the given transcripts, then we apply Doc2Vec method to represent each message as a vector that will be concatenated with LIWC and context features. The vectors are input data of Naïve Bayes algorithm; a machine learning method; that aims to classify transcripts according to cognitive presence categories.

scholarly journals The Role of Zoom Cloud Meeting as Learning Online Medium in Pandemic

2022 ◽  
Vol 1 (1) ◽  
pp. 26-34
Author(s):  
Dian Rianita ◽  
Indah Muzdalifah
Keyword(s):  
Online Learning ◽  
Teaching And Learning ◽  
Large Scale ◽  
Face To Face ◽  
Positive Side ◽  
Use Of Technology ◽  
Pros And Cons ◽  
Face To Face Learning ◽  
The Government ◽  
The Impact

Before the arrival of the Covid 19 virus epidemic, the government actually launched the industry 4.0 and 5.0 revolutions, including in the field of education where the use of technology as a medium or instrument in learning can be maximized to use. However, most of teacher and students were not able to conduct yet. When this epidemic comes, it can’t help using technology in the field of education ready or not. The government's policy to execute large-scale social restrictions or PSBB to prevent the transmission of the epidemic has made the implementation of the teaching and learning process online starting from elementary level to university. However, every change certainly causes pros and cons. There are both positive and negative sides to online learning. The positive side can make it easier and more efficient for face-to-face learning to be done from home anytime and anywhere. Meanwhile, the problems that arise include a lack of understanding in the use of technology, inadequate internet network facilities and many more effects as a result of this online learning. Therefore, this article discusses the phenomena that occur around education caused by the impact of the outbreak in the middle of the Covid 19 pandemic.

Parallel naive Bayes algorithm for large-scale Chinese text classification based on spark

2019 ◽  
Vol 26 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Peng Liu ◽  
Hui-han Zhao ◽  
Jia-yu Teng ◽  
Yan-yan Yang ◽  
Ya-feng Liu ◽  
...  
Keyword(s):  
Chinese Text ◽  
Text Classification ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Chinese Text Classification ◽  
Bayes Algorithm

scholarly journals IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK MEMPREDIKSI FREKUENSI TUNAI PADA MESIN ATM DI MASA TRANSISI PEMBATASAN SOSIAL BERSKALA BESAR (PSBB) PANDEMI COVID-19

2021 ◽  
Vol 4 (1) ◽  
pp. 47-52
Author(s):  
Saptari Wijaya Mulia ◽  
Sujiharno Sujiharno ◽  
Arief Wibowo
Keyword(s):  
Quality Of Service ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Seasonal Factors ◽  
Bayes Algorithm ◽  
The Right

Determining the need of money for ATM is usually different, that is one of the problems in managing money allocation of ATM. Some seasonal factors such as holidays and the implementation of transition large-scale social restrictions related to the covid-19 pandemic that can affect fluctuations in cash transactions. In this paper aims to determine the frequency of cash withdrawals at ATM since the enactment of transition large-scale social restrictions in Jakarta using the naive bayes algorithm so it can be identified which ATM require more allocation money or not. Providing the right money allocation can improve the quality of service to customers and minimize unused money in ATM. Results of analysis using a Naive Bayes algorithm to predict cash withdrawals frequencies at ATM that show a prediction accuracy up to 81%

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.

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