I Nyoman Switrayana

Work place: Department of Engineering, Universitas Bumigora, Mataram, Indonesia

E-mail: nyoman.switrayana@universitasbumigora.ac.id


Research Interests: Computer Vision, Machine Learning, Natural Language Processing, Deep Learning


I Nyoman Switrayana currently works as a Lecturer in the Department of Computer Science at Universitas Bumigora. He received his bachelor's degree in Computer Engineering (ST) from Telkom University, Bandung. His master's degree (MT) was obtained from the School of Electrical Engineering and Informatics, Bandung Institute of Technology. His areas of interest include Artificial Intelligence, Natural Language Processing, Computer Vision, Data Mining, Machine Learning, Deep Learning, and Recommender Systems. He can be contacted via email at nyoman.switrayana@universitasbumigora.ac.id

Author Articles
Enhancing Sentiment Analysis for the 2024 Indonesia Election Using SMOTE-Tomek Links and Binary Logistic Regression

By Neny Sulistianingsih I Nyoman Switrayana

DOI: https://doi.org/10.5815/ijeme.2024.03.03, Pub. Date: 8 Jun. 2024

The Indonesian Election is one of the most anticipated political contestations among the Indonesian people. Mainly because the results of the Indonesian Election are leaders in Indonesia ranging from governors and legislative members to the president and vice president of Indonesia, who will lead the next five years, considering the importance of the five-year agenda, the dissemination of good information about work programs, the activities of prospective leaders who will elect in the 2024 election and various news stories are starting to spread on Twitter. Based on this, this research aims to analyze public sentiment on Twitter wa The research method used is SMOTE-Tomek Links to overcome imbalanced data. In contrast, sentiment analysis uses Binary Logistic Regression. Evaluation related to this model measures accuracy and ROC Curves. The evaluation results show that the SMOTE-Tomek Links method is less than optimal for the data used in the research, namely the 2024 election data, with an accuracy value of 0.581 for training data and 0.406 for testing data. Undersampling methods such as Tomek Links and Random (undersampling) show higher values when combined with Binary Logistic Regression in analyzing the sentiment produced in this study, namely 0.983 and 0.938 for the Tomek Links method and 0.964 and 0.902 for the Random (undersampling) method, respectively -each for training and testing data.

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