scholarly journals PERBANDINGAN KERNEL SUPPORT VECTOR MACHINE (SVM) DALAM PENERAPAN ANALISIS SENTIMEN VAKSINISASI COVID-19

2021 ◽  
Vol 4 (2) ◽  
pp. 139-145
Author(s):  
Thalita Meisya Permata Aulia ◽  
Nur Arifin ◽  
Rini Mayasari
Keyword(s):  
Clinical Trial ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Research Data ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Kernel Space ◽  
Kernel Support Vector Machine ◽  
Pros And Cons

In early 2020, the first recorded death from the COVID-19 virus in China [3]. Followed by WHO which later stated that the COVID-19 virus caused a pandemic. Various efforts were made to minimize the transmission of COVID-19, such as physical distancing and large-scale social circulation. However, this resulted in a paralyzed economy, many factories or business shops closed, eliminating the livelihoods of many people. Vaccines may be a solution, various International Research Communities have conducted research on the COVID-19 vaccine. In early 2021 the Sinovac vaccine from China arrived in Indonesia and was declared a BPOM clinical trial, but the existence of the vaccine still raises pros and cons, some have responded well and others have not. For this reason, a sentiment analysis of the COVID-19 vaccine will be carried out by taking data from Twitter, then classified using the Support Vector Machine algorithm. The research data is nonlinear data so it requires a kernel space for the text mining process, while there has been no specific research regarding which kernel is good for sentiment analysis, so a test will be carried out to find the best kernel among linear, sigmoid, polynomial, and RBF kernels. The result is that sigmoid and linear kernels have a better value, namely 0.87 compared to RBF and polynomial, namely 0.86

scholarly journals Twitter Sentiment Analysis of Bangkok Tourism During COVID-19 Pandemic Using Support Vector Machine Algorithm

2021 ◽  
Vol 16 (1) ◽  
pp. 24-30
Author(s):  
Thanapat Sontayasara ◽  
Sirawit Jariyapongpaiboon ◽  
Arnon Promjun ◽  
Napat Seelpipat ◽  
Kumpol Saengtabtim ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Tourism Industry ◽  
Capital City ◽  
World Health ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Control And Prevention ◽  
Primary Focus ◽  
Health Organization

In the year 2020, SARS-CoV-2, the virus behind the coronavirus disease (COVID-19) pandemic, affected many lives and businesses worldwide. COVID-19, which originated in Wuhan City, China, at the end of December 2019, spread over the entire world in approximately four months. By October 2020, approximately 20 million people were infected and millions had died from this disease. Many health organizations such as the World Health Organization and Centers for Disease Control and Prevention made COVID-19 their primary focus. Many industries, especially, the tourism industry, were affected by the pandemic as many flight and hotel reservations were canceled. Thailand, a country considered one of the world’s most popular tourist destinations, suffered much losses because of this pandemic. Many events and travel bookings were canceled and/or postponed. Many people expressed their views and emotions related to this situation over social media, which is considered a powerful media for spreading news and information. In this research, the views of people who were planning to travel to Bangkok, the capital city and most popular destination in Thailand, were retrieved from Twitter for the dates between April 3 and 30, 2020, the period during which the country underwent nationwide lockdown. Sentiment analysis was performed using the support vector machine algorithm. The results showed 71.03% classification accuracy based on three sentiment classifications: positive, negative, and neutral. This study could thus provide an insight into travelers’ opinions and sentiments related to the tourism business. Based on the significant terms in each sentiment extracted, strengths and weaknesses of each tourism issue could be obtained, which could be used for making recommendations to the related tourism organizations.

scholarly journals USTADZ ABDUL SOMAD LECTURE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE ALGORITHM COMPARISON OF COMPARATIVE FEATURES SELECTION

2020 ◽  
Vol 16 (1) ◽  
pp. 111-116
Author(s):  
Dedi Aridarma ◽  
Rifki Sadikin ◽  
Bobby Suryo Prakoso ◽  
Heru Sukma Utama
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Information Gain ◽  
Particle Swarm ◽  
Support Vector ◽  
Features Selection ◽  
Support Vector Machine Algorithm ◽  
Swarm Optimization ◽  
Algorithm Comparison

Religious lectures are activities that are identical to the religious presentation, delivered verbally by a person who has religious knowledge and then delivered to the community with the aim of the knowledge delivered can be understood. Ustadz Abdul Somad was one of the preachers who had been known to various levels of society, but his lectures were not all acceptable to the people who liked or disliked those who came from various positive and negative comments on social media. To solve these problems, Sentiment Analysis was used by applying the Support Vector Machine Algorithm method. The purpose of this study is to compile using the selection of feature Particle Swarm Optimization and Information Gain. The results for Particle Swarm Optimization Selection Feature resulted in Accuracy of 80.57%, Precision of 85.45%, and Recall of 79.52%, Selection Feature Information Gain resulted in Accuracy of 79.78%, Precision of 78.47%, and Recall of 78, 43%, Based on the results of this study, it can be concluded that using the Particle Swarm Optimization selection feature is better at the level of accuracy when compared to using the Information Gain selection feature.

scholarly journals Text Mining Untuk Analisis Sentimen Pelanggan Terhadap Layanan Uang Elektronik Menggunakan Algoritma Support Vector Machine

2020 ◽  
Vol 9 (2) ◽  
pp. 247
Author(s):  
Fajar Romadoni ◽  
Yuyun Umaidah ◽  
Betha Nurina Sari
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Linear Kernel ◽  
Electronic Money ◽  
The People ◽  
Pros And Cons ◽  
Media Server ◽  
Negative Sentiment ◽  
Over Time

Electronic money is a cashless payment instrument whose money is stored in media server or chip that can be moved for the benefit of payment transactions or fund transfers. In Indonesia, there are already many electronic money products, one of which is OVO. OVO is very popular with the people of Indonesia because it offers many promos such as discounts and cashback. But over time, that much promotion is detrimental to OVO shareholders, so the portion of promo given by OVO to its customers is finally reduced. That incident caused many pros and cons opinions about OVO, one of them is on social media Twitter. Sentiment analysis can be used as a solution to process the opinions of OVO customers on Twitter. This study aims to classify the customer opinions on OVO services into positive and negative classes. This study uses the Support Vector Machine algorithm with 3852 data taken from Twitter with keyword @ovo_id using web scraping techniques. The dataset divided into two classes, 2034 positive and 1818 negative sentiment data. The classification process is carried out with four splitting data scenarios, with 60:40, 70:30, 80:20, 90:10 data ratio and with four kernel such as linear, rbf, sigomid, and polynomial. The final results show that the greatest accuracy value obtained by linear kernel with 90:10 data ratio which gets an accuracy value of 98.7%.

scholarly journals Analisis Sentimen Konten Radikal dalam Kontestasi Politik 2019 di Media Twitter Menggunakan Interjection dan Punctuation

Repositor ◽  
2020 ◽  
Vol 2 (7) ◽  
pp. 905
Author(s):  
Taufik Nurahman ◽  
Yufis Azhar ◽  
Nur Hayatin
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Support Vector ◽  
The Political ◽  
Support Vector Machine Algorithm ◽  
Political Actors ◽  
Radical Content

Sentiment analysis is now a trend to identify people's opinions and emotions in responding to a situation. In the political year, many opinions were scattered both written in print and social media. Political actors have different views, so that raises a lot of opinions that lead to radical actions such as SARA to people with different opinions. Research related to the analysis of radical sentiments via Twitter has been done by several researchers before, but there have been no studies of radical sentiment analysis using extraction features. This study proposes to conduct a radical content sentiment analysis on Indonesian textual tweets related to political contestation in Indonesia which then uses two features namely punctuation and interjection, and is classified using the support vector machine algorithm. From the results of the classification that has been done, obtained an accuracy value of 80% sentiment analysis and radical sentiment analysis conducted several times with a number of different interjection, obtained an accuracy of 94% using 200 interjection words. 

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