NAÏVE BAYES AND SUPPORT VECTOR MACHINE BASED ON OPTIMIZATION FOR PUBLIC SENTIMENT ANALYSIS POST-2024 ELECTION

Fari Katul Fikriah, Amelia Devi Putri Ariyanto

Abstract


The 2024 election has sparked an explosion of public opinion across various digital platforms, but the complexity and large volume of data make it difficult for policymakers to understand public sentiment in a timely manner. Therefore, an accurate and efficient sentiment analysis method is needed to automatically classify public opinion. This study aims to analyze and compare the performance of the Naïve Bayes algorithm and an optimized Support Vector Machine (SVM) in classifying post-election public sentiment. The research method includes collecting 10,000 text data entries from various data sources, conducting text preprocessing, extracting features using the TF-IDF method, applying both algorithms with parameter tuning, and generating their performance using accuracy, precision, recall, and F1 score metrics. The results show that the optimized SVM algorithm delivers superior performance, achieving 88.24% accuracy, compared to 82.35% for Naïve Bayes. These findings indicate that SVM is more effective in handling complex public opinion sentiment classification, thus serving as a valuable reference for post-election policymaking

Full Text:

PDF

References


L. Damayanti dan K. M. Lhaksmana, “Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter,” Jurnal dan Penelitian Teknik Informatika (SINKRON), vol. 8, no. 2, pp. 938-946, 2024.

E. Yulianti, “Sentiment Analysis of Tweets Before the 2024 Elections in Indonesia Using Bert Language Models,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 9, no. 3, 2023.

B. Bansal dan S. Srivastava, “On predicting elections with hybrid topic based sentiment analysis of tweets,” 3rd International Conference on Computer Science and Computational Intelligence 2018, vol. 135, pp. 346-353, 2018.

N. Mirantika, R. Trisudarno dan T. S. Syamfithriani, “Implementation of Naïve Bayes Algorithm for Early Detection of Stunting Risk,” Journal of Applied Informatics and Computing (JAIC), vol. 9, pp. 356-363, 2025.

G. A. Febriyanti dan A. Baita, “Comparison of Support Vector Machine and Decision Tree Algorithm Performance with Undersampling Approach in Predicting Heart Disease Based on Lifestyle,” Journal of Applied Informatics and Computing, vol. 9, pp. 318-327, 2025.

R. Putra, Y. Yusra dan M. Fikry, “Penerapan Metode SVM pada Klasifikasi Sentimen terhadap Anies Baswedan sebagai Bakal Calon Presiden 2024,” Jurnal Informatika Universitas Pamulang, vol. 8, pp. 145-152, 2023.

M. Sakhdiah, A. Salma, D. Permana dan D. Fitria, “Sentiment Analysis Using Support Vector Machine (SVM) of ChatGPT Application Users in Play Store,” UNP Journaal of Statistics and Data Science, vol. 2, pp. 151-158, 2024.

T. A. Amini dan K. Setiawan, “Application of the Naive Bayes Algorithm in Twitter Sentiment Analysis of 2024 Vice Presidential Candidate Gibran Rakabuming Raka using Rapidminer,” nternational Journal Software Engineering and Computer Science (IJSECS), vol. 4, no. 1, pp. 234-246, 2024.

N. Mirantika, R. Trisudarmo dan T. S. Syamfithriani, “Implementation of Naïve Bayes Algorithm for Early Detection of Stunting Risk,” Journal of Applied Informatics and Computing (JAIC), vol. 9, pp. 356-363, 2025.

G. A. P. Febriyanti dan A. Baita, “Comparison of Support Vector Machine and Decision Tree Algorithm Performance with Undersampling Approach in Predicting Heart Disease Based on Lifestyle,” Journal of Applied Informatics and Computing, vol. 9, pp. 318-327, 2025.

P. N. Wear , Nasruddin dan I. Rosita, “Analisis Sentimen Terhadap Hasil Pilpres 2024 pada Aplikasi Tiktok dan X Menggunakan Metode Naive Bayes Classifier,” Jurnal Ilmiah Informatika Mulia, vol. 1, 2024.

O. Peretz, M. Koren dan O. Koren, “Naive Bayes classifier – An ensemble procedure for recall and precision enrichment,” Engineering Applications of Artificial Intelligence, vol. 136, pp. 1-12, 2024.

A. Z. Arrayyan, H. Setiawan dan K. T. Putra, “Naive Bayes for Diabetes Prediction: Developing a Classification Model for Risk Identification in Specific Populations,” Semesta Teknika, vol. 27, pp. 28-36, 2024.

D. Srivastava dan L. Bhambhu, “Data classification using support vector machine,” Journal of Theoretical and Applied Information Technology, vol. 12, pp. 1-7, 2010.

S. A. Salleh, N. Khalid, N. Danny dan N. A. M. Zaki, “Support Vector Machine (SVM) and Object Based Classification in Earth Linear Features Extraction: A Comparison,” Revue internationale de géomatique, vol. 33, pp. 183-199, 2024.

T. H. Tanjung dan M. Furqan, “Classification of Heart Disease Using Support Vector Machine,” Sinkron : Jurnal dan Penelitian Teknik Informatika, vol. 8, pp. 1803-1812, 2024.

G. F. Nama, “Implementation of Naïve Bayes Classifier & Support Vector Machine Algorithm for Sentiment Classification using Twitter Data on Indonesian Presidential Candidates In 2024,” Journal of Information Systems Engineering & Management , vol. 10, pp. 510-531, 2025.




DOI: https://doi.org/10.33387/jiko.v8i2.10147

Refbacks

  • There are currently no refbacks.