Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine

Hermawan, Aditiya; Jowensen, Indrico; Junaedi, Junaedi; Edy,

doi:10.23887/jstundiksha.v12i1.52358

Cited by 3 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first step in preprocessing is Tokenizing, which separates words in a sentence to facilitate further text analysis. The words produced by this process are called tokens, and they are useful when extracting meaning from text [18]. For example, we can use tokens to detect nouns and verbs in a sentence or to identify the names of people mentioned.…”

Section: System Implementationmentioning

confidence: 99%

Analyzing Public Trust in Presidential Election Surveys: A Study Using SVM and Logistic Regression on Social Media Comments

Afandi,

Isnaini

2024

J. Comput. Sci. Eng

View full text Add to dashboard Cite

In the context of democracy in Indonesia, elections play a crucial role, and survey agencies often publish their results on social media. User responses, especially from voters, often express dissatisfaction, including distrust, insults, and negative comments, if the candidate they support receives low survey results. Therefore, this study aims to examine the level of public trust in the survey results of Presidential candidates in 2024 using the Support Vector Machine (SVM) and Logistic Regression algorithms. The study utilized data from 1778 Instagram comments and 985 Twitter tweets. The process involved problem identification, data collection, and system implementation, such as preprocessing, labeling, SMOTE, TF-IDF, data splitting, model classification, and evaluation. The results show that SVM with an 80% training data and 20% test data scenario provides high accuracy, namely 93.19% from Instagram and 91.19% from Twitter. Logistic Regression, with the highest accuracy of 89.79% from Instagram and 88.01% from Twitter in the same scenario. Sentiment analysis using SVM scenario one resulted in 195 positive comments and 216 negative comments. Logistic Regression scenario one shows 180 positive sentiments and 216 negative sentiments. From the classification results, it can be concluded that the level of public trust tends to be negative towards the survey results of the 2024 Presidential candidates, both using SVM and Logistic Regression.

show abstract

Section: System Implementationmentioning

confidence: 99%

Analyzing Public Trust in Presidential Election Surveys: A Study Using SVM and Logistic Regression on Social Media Comments

Afandi,

Isnaini

2024

J. Comput. Sci. Eng

View full text Add to dashboard Cite

show abstract

“…Text Mining is the search for unknown information using automated data extraction from large amounts of unstructured text [18]. The purpose of text mining is to analyze the opinions, feelings, judgments, attitudes, assessments, feelings of a person to find out whether a topic, service, organization or person is related to a particular activity [19].…”

Section: F Text Miningmentioning

confidence: 99%

Sentiment Analysis of Google Play Store User Reviews on Digital Population Identity App Using K-Nearest Neighbors

Kurniawan,

Wijaya,

Aprisusanti

2024

SISFOKOM

View full text Add to dashboard Cite

The Digital Population Identity Application provides convenience for users to access and manage their population data digitally. Based on the increasing usage of the Digital Population Identity Application on the Google Play Store, various user reviews of the application have emerged. Therefore, sentiment analysis is needed to provide a deeper understanding of user perceptions and to classify user reviews of the Digital Population Identity Application. Sentiment analysis is a computational study of opinions, feelings, and emotions expressed in text, using the K-Nearest Neighbors method, which is a classification method based on the closest distance or similarity to objects in the training data. Using 5000 relevant review data from September 2022 to December 2023, after labeling them into positive, negative, and neutral sentiment classes, the results show 3581 negative sentiments, 1031 positive sentiments, and 388 neutral sentiments. Testing was conducted by applying the K-Nearest Neighbors method in the classification stage, testing this method by varying K values from 1 to 10. The best results were obtained with a training data ratio of 90% to testing data ratio of 10%. The best results were achieved at K values of 8, 9, and 10, with an accuracy of 81%, precision of 82%, recall of 95%, and an F1-Score of 88%. With a training data ratio of 70% to testing data ratio of 30%, the best results were obtained at K values of 6, 7, 8, 9, and 10, with an accuracy of 80%, precision of 81%, recall of 95%, and an F1-Score of 88%. Based on the results of this research, the K-Nearest Neighbors method can be used for sentiment classification of user reviews with good results.

show abstract

Analisis Sentimen pada Aplikasi Translate Google Menggunakan Metode SVM (Studi Kasus: Komentar Pada Playstore)

Ashari,

Saputra,

Larosa

et al. 2023

View full text Add to dashboard Cite

This research aims to analyze reviews in understanding the opinions and emotions expressed by users regarding the Google Translate application on the Google Play Store using sentiment analysis. By using the Support Vector Machine (SVM) method in sentiment analysis of the Google Translate application, to get a better understanding of how users respond to the application. This can help developers improve the application experience, responding better to user needs and preferences. This user review analysis uses the SVM method. The measuring tool in this research uses, firstly, the Indonesian Lexicon as a tool to obtain positive and negative results, secondly, term frequency–inverse document frequency (tf-idf) as a support for the results of the evaluation. Google Translate app has a dataset of 1000 user reviews collected from Google play store. The results of analysis using Support Vector Machine produced 95% accuracy, with "no" as the result of the most positive and negative reviews out of 1580 reviews.

show abstract

Implementasi Text-Mining untuk Analisis Sentimen pada Twitter dengan Algoritma Support Vector Machine

Cited by 3 publications

References 27 publications

Analyzing Public Trust in Presidential Election Surveys: A Study Using SVM and Logistic Regression on Social Media Comments

Analyzing Public Trust in Presidential Election Surveys: A Study Using SVM and Logistic Regression on Social Media Comments

Sentiment Analysis of Google Play Store User Reviews on Digital Population Identity App Using K-Nearest Neighbors

Analisis Sentimen pada Aplikasi Translate Google Menggunakan Metode SVM (Studi Kasus: Komentar Pada Playstore)

Contact Info

Product

Resources

About