Implementation of Stacking Ensemble Classifier for Multi-class Classification of COVID-19 Vaccines Topics on Twitter

Jayapermana, Rama; Aradea, Aradea; Kurniati, Neng Ika

doi:10.15294/sji.v9i1.31648

Cited by 9 publications

(15 citation statements)

References 9 publications

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“…The results show significant improvements in accuracy and F1score compared to other models, even with less data. This research highlights the importance of ensemble classifiers in analyzing public opinion on social media related to public health issues [22].…”

Section: Literature Reviewmentioning

confidence: 86%

Stacking classifier method for prediction of human body performance

Octavianto,

Wibowo

2024

IJEECS

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<p>A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data.</p>

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Section: Literature Reviewmentioning

confidence: 86%

Stacking classifier method for prediction of human body performance

Octavianto,

Wibowo

2024

IJEECS

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“…Based on literature studies, most of the previous studies were only related to one SDGs point. SDGs 1 [14], [15]; SDGs 2 [16], [17]; SDGs 3 [4], [18], [19], [20], [21], [22]; SDGs 4 [6], [23], [24], [25], [26]; SDGs 5 [27], [28]; SDGs 7 [29], [30]; SDGs 8 [3], [8], [31], [32]; SDGs 9 [33]; SDGs 10 [34], [35]; SDGs 11. [36]; SDGs 12 [37], [38]; SDGs 13 [39], [40]; SDGs 14 [41], [42]; SDGs 16 [43]; and SDGs 17 [44].…”

Section: Methodsmentioning

confidence: 99%

“…Flores et al [24] used SMOTE to get higher accuracy, concluding that using SMOTE increased accuracy in using the K-12 program dataset in the Philippines. Jayapermana et al [20] used the Ensemble Machine Learning Classifier to research the COVID-19 vaccine. This resulted in the conclusion that using the stacking ensemble classifier obtained higher accuracy than using only a single algorithm.…”

Section: Methodsmentioning

confidence: 99%

Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia

Gufroni,

Hoeronis,

Fajar

et al. 2024

JOIV : Int. J. Inform. Visualization

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As part of the Sustainable Development Goals (SDGs), governments worldwide have committed to improving people's lives to improve the quality of life for all, including the 17 such goals that were agreed upon in 2015 to benefit the human race as a whole. It would be interesting to see how society responds to the SDGs after approximately half of them have been achieved. This public response was analyzed in terms of sentiment. Within the total number of internet users in Indonesia, there are 18.45 million Twitter users. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. The platform enables anyone to write about anything they are experiencing in their lives, such as what is happening in their environment, what is happening in their education system, what is happening in the food industry, how people feel, and many more. To model the data collected, the researchers used Ensemble Machine Learning Classifiers (EMLC) to model the data by using a machine learning classifier that uses machine learning techniques. The best model in this study is EMLC-Stacking with a data splitting of 80:20 and using SMOTE, which obtains an accuracy of 91%. This accuracy results from a 5% increase compared to when not using SMOTE. From 15,698 tweets, this research found that 47% were positive sentiments, 28% were negative sentiments, and 25% were neutral sentiments. The results that we measured offer hope that there will be a positive trend in the journey of the SDGs until 2030 if these findings are true.

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“…The proposed model in this research uses an Ensemble Learning algorithm [30]- [32] that uses a voting system to get the best results. There are several methods combined into this model, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM).…”

Section: Ensemble Learningmentioning

confidence: 99%

Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding

Ningsih,

Wibowo,

Dullah

et al. 2023

J. Soft Comput. Explor.

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The issue of the Global Recession is hitting various countries, including Indonesia. Many Indonesians have expressed their opinions on the issue of the global recession in 2023, one of which is from Twitter. By understanding public sentiment, we can assess the impact felt by the public on the issue itself. Sentiment analysis in this research is a form of support to evaluate Indonesia's sustainability in dealing with the issue of Global Recession in accordance with the Sustainable Development Goals (SDGs). However, in previous research, it is still rare to find a model that has good performance in conducting Global Recession Sentiment Analysis. Therefore, the purpose of this research is to propose a machine learning model that is expected to provide good performance in sentiment analysis. The existing sentiment dataset is labeled with the Valence Aware Dictionary for Social Reasoning (VADER) algorithm, then an Ensemble Learning method is designed which is composed of Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms. After that, the Countvectorizer feature extraction with N-Gram, Best Match 25 (BM25), and Word Embedding is carried out to convert sentences in the dataset into numerical vectors so as to improve model performance. The research results provide a more optimal accuracy performance of 95.02% in classifying sentiment. So that the proposed model successfully performs sentiment analysis better than previous research.

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Implementation of Stacking Ensemble Classifier for Multi-class Classification of COVID-19 Vaccines Topics on Twitter

Cited by 9 publications

References 9 publications

Stacking classifier method for prediction of human body performance

Stacking classifier method for prediction of human body performance

Implementation of Ensemble Machine Learning Classifier and Synthetic Minority Oversampling Technique for Sentiment Analysis of Sustainable Development Goals in Indonesia

Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding

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