Sentiment Analysis Pedulilindungi Tweet Using Support Vector Machine Method

Darusman, Farhan Setiyo; Arifiyanti, Amalia Anjani; Wati, Seftin Fitri Ana

doi:10.33086/atcsj.v4i2.2836

Cited by 5 publications

(2 citation statements)

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“…The system design of the hoax detection is shown in Figure 1. The data for this research was collected from Indonesian-version of Twitter, using the tools available in the Python programming language, namely snscrape [15]. The said data was taken and gathered from a collection of tweets.…”

Section: Methodsmentioning

confidence: 99%

Detecting Fake News on Social Media Combined with the CNN Methods

Fauzy

Setiawan

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Social media platforms are created to facilitate human social life as technology develops. Twitter is one of the most popular and frequently used social media for exchanging information. This social media platform disseminates real-time and complete information. Unfortunately, there are not a few tweets that contain false information or are often referred to as hoaxes. Those hoaxes that existed on Twitter are very troubling for society. Fake news or hoaxes can cause misunderstandings in receiving information. Therefore, this research aimed at developing a system that can detect hoaxes on Twitter to anticipate their spread, which can be detrimental to related parties. The system being developed uses a deep learning approach with the Convolutional Neural Network (CNN), Term Frequency-Inverse Document Frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT), and Global Vectors (GloVe). The results of this study display the fake news detected by the system using the CNN method with baseline, BERT, and GloVe. The data have been adjusted to the keywords related to fake news and spread on online media, such as Hoax or Not from Detik.com, CekFakta from Kompas.com, etc. The results show the highest accuracy of 98.57% using CNN with a split ratio of 90:10, baseline unigram-bigram, BERT, and Top10 corpus tweet+IndoNews with an increase of 4.7%.

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Section: Methodsmentioning

confidence: 99%

Detecting Fake News on Social Media Combined with the CNN Methods

Fauzy

Setiawan

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…The data used were 2320 instances, and the results of his research obtained an f1 score of 95.13%. Research [14] used the SVM method for sentiment analysis of user reviews on the PeduliLindungi application with an accuracy of 64%. Research [15] used the C4.5 method to classify the nutritional status of toddlers with an accuracy of 95%.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Methods for Classifying User Satisfaction Opinions of the PeduliLindungi Application

Tisna Putra,

Anggrawan,

Hairani

2023

matrik

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Since the emergence of the Covid-19 virus, the Indonesian government urged people to study, work, and worship or work from home. The social restriction policy has changed people's behavior which requires physical distance in social interaction. The government developed an application to minimize the spread of Covid-19, namely the PeduliLindungi application. The PeduliLindungi application is a tracking application to prevent the spread of Covid-19. The government's policy of implementing the PeduliLindungi application during Covid-19 aroused pros and cons from the public. The volume of PeduliLindungi application review data on Google Play was increasing, so manual analysis could not be done. New analytical approaches needed to be carried out, such as sentiment analysis. This research aimed to analyze user reviews of the PeduilLindungi application using classification methods, namely Support Vector Machine (SVM), Random Forest, and Naïve Bayes. The methods used were Synthetic Minority Oversampling Technique (SMOTE), Random Forest, SVM, and Naïve Bayes. SMOTE was used to balance user review data on the PeduliLindungi application. After the data had been balanced, classification was carried out. The results of this study showed that the Random Forest method with SMOTE got better accuracy than the SVM and Naive Bayes methods, which was 96.3% based on the division of training and testing data using 10-fold cross-validation. Thus, using the SMOTE method could improve the accuracy of classification methods in classifying opinions of user satisfaction with the PeduliLindungi application.

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