Sentiment Analysis On Indonesian Tweets about the 2024 Election

Authors

  • Alfan Ramadhan Sembiring Department of Informatics, Universitas Islam Indonesia, Yogyakarta, Indonesia
  • Chandra Kusuma Dewa Department of Informatics, Universitas Islam Indonesia, Yogyakarta, Indonesia

DOI:

10.33395/sinkron.v9i1.14481

Keywords:

deep learning, gru, lstm,machine learning, naive bayes, sentiment analysis, svm

Abstract

This study investigates public sentiment on Indonesian Twitter regarding the 2024 General Election, employing machine learning and deep learning techniques, including Naïve Bayes, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The dataset was collected using a Tweet Harvest method with the keyword "Pemilu" and underwent preprocessing steps such as case folding, removal of symbols and URLs, stopword elimination, and tokenization to ensure data quality. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were applied to assess the models' effectiveness. Naïve Bayes achieved the highest accuracy of 64%, followed by SVM at 63%, LSTM at 60%, and GRU at 57%. The findings indicate that traditional models like Naïve Bayes and SVM perform effectively on smaller datasets with structured features, while deep learning models excel in capturing complex sequential dependencies. However, deep learning methods exhibited overfitting tendencies, indicating the need for better regularization and optimization techniques. Furthermore, it emphasizes the potential of integrating traditional algorithms with advanced methods to enhance sentiment classification accuracy and generalizability across diverse datasets.

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How to Cite

Sembiring, A. R., & Dewa, C. K. . (2025). Sentiment Analysis On Indonesian Tweets about the 2024 Election. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(1), 413-422. https://doi.org/10.33395/sinkron.v9i1.14481

Issue

Vol. 9 No. 1 (2025): Research Article, January 2025

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Articles

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Copyright (c) 2025 Alfan Ramadhan Sembiring, Chandra Kusuma Dewa

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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