Application of machine learning for election data classification in Tegal city based on political party support
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Abstract
Elections are a critical aspect of democracy, where voter sentiment and political party support significantly influence outcomes. This study aims to predict election results in Tegal City using machine learning models, specifically Neural Networks, Random Forest, and Naive Bayes. Each algorithm was applied to a dataset containing demographic, polling, and Sentiment data to analyze political party support. The research revealed that Neural Networks outperformed other models in terms of accuracy (92%) and F1 scores for both positive (91%) and negative sentiments (92%). Random Forest and Naive Bayes, while effective, displayed lower overall performance. The findings highlight the value of utilizing advanced algorithms for local election sentiment analysis to help candidates adjust campaign strategies. This approach enhances understanding of voter behavior and supports more informed decision-making processes for the public and policymakers
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Andriani, W., Gunawan, G., Naja, N. N. P. W., & Anandianskha, S. . (2024). Application of machine learning for election data classification in Tegal city based on political party support . Journal of Intelligent Decision Support System (IDSS), 7(4). Retrieved from https://idss.iocspublisher.org/index.php/jidss/article/view/270
References
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Shen, K., Guo, J., Tan, X., Tang, S., Wang, R., & Bian, J. (2023). A Study on ReLU and Softmax in Transformer. http://arxiv.org/abs/2302.06461
Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. https://doi.org/https://doi.org/10.1016/j.asoc.2019.105524
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Tahyudin, I., Hananto, A. R., Rahayu, S. A., Anjani, R. M., & Nurhopipah, A. (2023). Sentiment Analysis Model Development on E-Money Service Complaints. TEM Journal, 12(4), 2050–2055. https://doi.org/10.18421/TEM124-15
Tsai, M.-H., Wang, Y., Kwak, M., & Rigole, N. (2019). A Machine Learning Based Strategy for Election Result Prediction. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), 1408–1410. https://doi.org/10.1109/CSCI49370.2019.00263
Valdivia, A., Luzón, M. V., Cambria, E., & Herrera, F. (2018). Consensus vote models for detecting and filtering neutrality in sentiment analysis. Information Fusion, 44, 126–135. https://doi.org/https://doi.org/10.1016/j.inffus.2018.03.007
Vanslette, K., Tohme, T., & Youcef-Toumi, K. (2020). A general model validation and testing tool. Reliability Engineering & System Safety, 195, 106684. https://doi.org/https://doi.org/10.1016/j.ress.2019.106684
Wardoyo, R., Musdholifah, A., Pradipta, G. A., & Sanjaya, I. N. H. (2020). Weighted Majority Voting by Statistical Performance Analysis on Ensemble Multiclassifier. 2020 Fifth International Conference on Informatics and Computing (ICIC), 1–8. https://doi.org/10.1109/ICIC50835.2020.9288552
Wongkar, M., & Angdresey, A. (2019). Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter. 2019 Fourth International Conference on Informatics and Computing (ICIC), 1–5. https://doi.org/10.1109/ICIC47613.2019.8985884
Yacouby, R., & Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. 79–91. https://doi.org/10.18653/v1/2020.eval4nlp-1.9
Yu, L., Hu, Y., Xie, X., Lin, Y., & Hong, W. (2020). Complex-Valued Full Convolutional Neural Network for SAR Target Classification. IEEE Geoscience and Remote Sensing Letters, 17(10), 1752–1756. https://doi.org/10.1109/LGRS.2019.2953892
Aloysius, C., & Tamil Selvan, P. (2023). Reduction of false negatives in multi-class sentiment analysis. Bulletin of Electrical Engineering and Informatics, 12(2), 1209–1218. https://doi.org/10.11591/eei.v12i2.4682
Badian, M., & Markovitch, S. (2020). Knowledge-Based Learning through Feature Generation. http://arxiv.org/abs/2006.03874
Chiong, R., & Theng, L. B. (2008). A hybrid Naive Bayes approach for information filtering. 2008 3rd IEEE Conference on Industrial Electronics and Applications, 1003–1007. https://doi.org/10.1109/ICIEA.2008.4582666
Daday, M. J. A., Fajardo, A. C., & Medina, R. P. (2019). Enhancing Feed-Forward Neural Network in Image Classification. ICCBD 2019. https://api.semanticscholar.org/CorpusID:209450429
Date, P., Arthur, D., & Pusey-Nazzaro, L. (2021). QUBO formulations for training machine learning models. Scientific Reports, 11(1), 1–10. https://doi.org/10.1038/s41598-021-89461-4
Dharmasaputro, A. A., Fauzan, N. M., Kallista, M., Wibawa, I. P. D., & Kusuma, P. D. (2022). Handling Missing and Imbalanced Data to Improve Generalization Performance of Machine Learning Classifier. 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), 140–145. https://doi.org/10.1109/ISMODE53584.2022.9743022
Djumadin, Z. (2021). Student Political Participation and the Future of Democracy in Indonesia. AL-ISHLAH: Jurnal Pendidikan, 13(3), 2399–2408. https://doi.org/10.35445/alishlah.v13i3.1438
El-Nasr, M. S., Dinh, T. H. N., Canossa, A., & Drachen, A. (2021). 219Supervised Learning in Game Data Science: Model Validation and Evaluation. In M. S. El-Nasr, A. Canossa, T.-H. D. Nguyen, & A. Drachen (Eds.), Game Data Science (p. 0). Oxford University Press. https://doi.org/10.1093/oso/9780192897879.003.0008
Fachrie, M. (2020). Machine Learning for Data Classification in Indonesia Regional Elections Based on Political Parties Support. Jurnal Ilmu Komputer Dan Informasi, 13(2), 89–96. https://doi.org/10.21609/jiki.v13i2.860
Gao, W., Xu, F., & Zhou, Z.-H. (2022). Towards convergence rate analysis of random forests for classification. Artificial Intelligence, 313, 103788. https://doi.org/https://doi.org/10.1016/j.artint.2022.103788
Gemp, I., Theocharous, G., & Ghavamzadeh, M. (2017). Automated Data Cleansing through Meta-Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(2), 4760–4761. https://doi.org/10.1609/aaai.v31i2.19107
Hadiati, T. L., Nugroho, H., & Utomo, D. T. B. (2022). Voters' Political Participation in the Covid-19 Pandemic According to the Geography and Topography Condition of the Region (Study on the 2020 Regional Head Election in Pekalongan Regency). Politik Indonesia: Indonesian Political Science Review, 7(3), 391–407. https://doi.org/10.15294/ipsr.v7i3.40812
Hammad, I., & El-Sankary, K. (2019). Practical considerations for accuracy evaluation in sensor-based machine learning and deep learning. Sensors (Switzerland), 19(16), 1–13. https://doi.org/10.3390/s19163491
Kalcheva, N., Marinova, G., & Todorova, M. (2023). Comparative Analysis of the Bernoulli and Multinomial Naive Bayes Classifiers for Text Classification in Machine Learning. 2023 International Conference Automatics and Informatics (ICAI), 28–31. https://doi.org/10.1109/ICAI58806.2023.10339077
Minh, T. N., Sinn, M., Lam, H. T., & Wistuba, M. (2018). Automated Image Data Preprocessing with Deep Reinforcement Learning. 1–9. http://arxiv.org/abs/1806.05886
Mohandoss, D. P., Shi, Y., & Suo, K. (2021). Outlier Prediction Using Random Forest Classifier. 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), 27–33. https://doi.org/10.1109/CCWC51732.2021.9376077
Myilvahanan, K., Y., P., Pasha, S., Ismail, M., & Tharun, V. (2023). A Study on Election Prediction using Machine Learning Techniques. 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), 1518–1520. https://doi.org/10.1109/ICAIS56108.2023.10073693
Nafiah, A., & Hidayat, N. A. (2021). COVID-19 Pandemic and Simultaneous Regional Head Elections in Indonesia. Indonesian Journal of Law and Society, 2(2), 145. https://doi.org/10.19184/ijls.v2i2.24661
Nargesian, F., Samulowitz, H., Khurana, U., Khalil, E. B., & Turaga, D. (2017). Nargesian 等。 - 2017 - Learning Feature Engineering for Classification.pdf. International Joint Conferences on Artifical Intelligence (IJCAI), 2529–2535.
Puspitasari, S. H., & Ali, M. (2023). Strengthening Democratic Elections and Quality in Indonesia. International Journal of Social Science, Education, Communication and Economics (SINOMICS JOURNAL), 1(6), 799–808. https://doi.org/10.54443/sj.v1i6.88
Raschka, S. (2018). Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning. http://arxiv.org/abs/1811.12808
Rodríguez, P., Bautista, M. A., Gonzàlez, J., & Escalera, S. (2018). Beyond one-hot encoding: Lower dimensional target embedding. Image and Vision Computing, 75, 21–31. https://doi.org/10.1016/j.imavis.2018.04.004
Shen, K., Guo, J., Tan, X., Tang, S., Wang, R., & Bian, J. (2023). A Study on ReLU and Softmax in Transformer. http://arxiv.org/abs/2302.06461
Singh, D., & Singh, B. (2020). Investigating the impact of data normalization on classification performance. Applied Soft Computing, 97, 105524. https://doi.org/https://doi.org/10.1016/j.asoc.2019.105524
Sutjiatmi, S., Akta Padma Eldo, D. H., & Zainudin, A. (2020). Public Perception Regarding Money Politics in General Election 2019 (Compartive Study on Tegal City and Tegal Regency). CosmoGov, 6(1), 61. https://doi.org/10.24198/cosmogov.v6i1.26632
Tae, K. H., Roh, Y., Oh, Y. H., Kim, H., & Whang, S. E. (2019). Data cleaning for accurate, fair, and robust models: A big data - AI integration approach. Proceedings of the ACM SIGMOD International Conference on Management of Data. https://doi.org/10.1145/3329486.3329493
Tahyudin, I., Hananto, A. R., Rahayu, S. A., Anjani, R. M., & Nurhopipah, A. (2023). Sentiment Analysis Model Development on E-Money Service Complaints. TEM Journal, 12(4), 2050–2055. https://doi.org/10.18421/TEM124-15
Tsai, M.-H., Wang, Y., Kwak, M., & Rigole, N. (2019). A Machine Learning Based Strategy for Election Result Prediction. 2019 International Conference on Computational Science and Computational Intelligence (CSCI), 1408–1410. https://doi.org/10.1109/CSCI49370.2019.00263
Valdivia, A., Luzón, M. V., Cambria, E., & Herrera, F. (2018). Consensus vote models for detecting and filtering neutrality in sentiment analysis. Information Fusion, 44, 126–135. https://doi.org/https://doi.org/10.1016/j.inffus.2018.03.007
Vanslette, K., Tohme, T., & Youcef-Toumi, K. (2020). A general model validation and testing tool. Reliability Engineering & System Safety, 195, 106684. https://doi.org/https://doi.org/10.1016/j.ress.2019.106684
Wardoyo, R., Musdholifah, A., Pradipta, G. A., & Sanjaya, I. N. H. (2020). Weighted Majority Voting by Statistical Performance Analysis on Ensemble Multiclassifier. 2020 Fifth International Conference on Informatics and Computing (ICIC), 1–8. https://doi.org/10.1109/ICIC50835.2020.9288552
Wongkar, M., & Angdresey, A. (2019). Sentiment Analysis Using Naive Bayes Algorithm Of The Data Crawler: Twitter. 2019 Fourth International Conference on Informatics and Computing (ICIC), 1–5. https://doi.org/10.1109/ICIC47613.2019.8985884
Yacouby, R., & Axman, D. (2020). Probabilistic Extension of Precision, Recall, and F1 Score for More Thorough Evaluation of Classification Models. 79–91. https://doi.org/10.18653/v1/2020.eval4nlp-1.9
Yu, L., Hu, Y., Xie, X., Lin, Y., & Hong, W. (2020). Complex-Valued Full Convolutional Neural Network for SAR Target Classification. IEEE Geoscience and Remote Sensing Letters, 17(10), 1752–1756. https://doi.org/10.1109/LGRS.2019.2953892
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