Sentiment Analysis of the 2024 Indonesian Presidential Dispute Trial Election using SVM and Naïve Bayes on Platform X
-
Zahra Nabila Maharani
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Ardytha Luthfiarta Universitas Dian Nuswantoro, Semarang, Indonesia
- Nabila Zibriza Farsya Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
Indonesian presidential dispute trial election are crucial activities in the democratic process where open exchanges of views and opinions occur. Sentiment analysis can help understand public opinion regarding these sessions. This study aims to conduct sentiment analysis of the 2024 Indonesian presidential dispute trial election using the Support Vector Machine (SVM) and Gausian Naïve Bayes (GNB) with Nazief Adriani and Sastrawi stemming methods on Platform X. The research addresses the challenge of uncertainty in interpreting public sentiment towards Indonesian presidential dispute trial election. SVM and GNB was chosen for its ability to classify large and complex data sets. The Nazief Andriani and Sastrawi stemming techniques were employed to reduce words to their base forms, thereby enhancing the quality of text analysis. The study was conducted on Platform X, which provides access to text data from various sources including social media and news platforms. The data used covered specific periods before, during, and after Indonesian presidential dispute trial election. The keywords used for the crawling process are “sidang sengketa pilpress”, “sidang sengketa pemilu”, and “sidang pilpres”. The classification technique is carried out by classifying it into two classes, namely positive and negative. In applying sentiment analysis using machine learning methods, there are several methods that are often used. Based on the results comparation of tests carried out on 2,443 tweets using SVM with Sastrawi stemming method produce the best accuracy of 91.1%, precision 90%, recall 91%., and F1-Score 91%.
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References
Z. Zainurohmah and A. Z. S. A. Sabri, “Presidential System in Indonesia Semi-Presidential System in France: How is Democracy Reflected in the Government Systems of the Two Countries?,” Indones. State Law Rev., vol. 6, no. 1, pp. 75–106, 2023, doi: 10.15294/islrev.v6i1.68226.
T. Taufiqurrahman, S. Hasanah, and F. Arzhi Jiwantara, “Sistem Penyelesaian Sengketa Pemilihan Umum Di Negara Hukum Demokrasi (Studi Komparatif),” JATISWARA, vol. 38, no. 2, pp. 241–254, 2023, doi: 10.29303/jtsw.v38i2.527.
S. Gardbaum, “Comparative political process theory,” Int. J. Const. Law, vol. 18, no. 4, pp. 1429–1457, 2020, doi: 10.1093/icon/moaa084.
T. D. Larassetya, A. Suryasuciramdhan, N. U. Salsa, and I. S. Aeni, “Analisis Opini Publik Terhadap Pemilu 2024 Pada Media Sosial X,” TUTURAN J. Ilmu Komunikasi, Sos. dan Hum., vol. 2, no. 2, pp. 292–301, 2024, doi: 10.47861/tuturan.v2i2.994.
D. Domke and K. Coe, The God Strategy: How Religion Became a Political Weapon in America. Oxford University Press, 2008.
A. Muzaki and A. Witanti, “SENTIMENT ANALYSIS OF THE COMMUNITY IN THE TWITTER TO THE 2020 ELECTION IN PANDEMIC COVID-19 BY METHOD NAIVE BAYES CLASSIFIER,” J. Tek. Inform., vol. 2, no. 2, pp. 101–107, 2021, doi: 10.20884/1.jutif.2021.2.2.51.
O. Manullang, C. Prianto, and N. H. Harani, “ANALISIS SENTIMEN UNTUK MEMPREDIKSI HASIL CALON PEMILU PRESIDEN MENGGUNAKAN LEXICON BASED DAN RANDOM FOREST,” J. Ilm. Inform., vol. 11, no. 02, pp. 159–169, 2023, doi: 10.33884/jif.v11i02.7987.
H. Zhu, “Sentiment analysis of 2021 Canadian election tweets,” in Proc.SPIE, Mar. 2023, vol. 12588, p. 9, doi: 10.1117/12.2667211.
A. Agarwal and V. Bansal, “Exploring sentiments of voters through social media content: A case study of 2017 assembly elections of three states in India,” in ICEIS 2020 - Proceedings of the 22nd International Conference on Enterprise Information Systems, 2020, vol. 1, no. Iceis, pp. 596–602, doi: 10.5220/0009517105960602.
G. Nugroho, D. T. Murdiansyah, and K. M. Lhaksmana, “Analisis Sentimen Pemilihan Presiden Amerika 2020 di Twitter Menggunakan Naïve Bayes dan Support Vector Machine,” e-Proceeding Eng., vol. 8, no. 5, pp. 10106–10115, 2021, doi: 16.36844/epe.v8i5.6387.
L. Damayanti and K. M. Lhaksmana, “Sentiment Analysis of the 2024 Indonesia Presidential Election on Twitter,” Sinkron, vol. 8, no. 2, pp. 938–946, 2024, doi: 10.33395/sinkron.v8i2.13379.
N. Dhamayanthi and B. Lavanya, “The Role Of Naïve Bayes, SVM, And Decision Trees In Sentiment Analysis,” Theory Pract., vol. 2024, no. 4, pp. 6377–6381, 2024, doi: 10.53555/kuey.v30i4.2392.
D. Mustikasari, I. Widaningrum, R. Arifin, and W. H. E. Putri, “Comparison of Effectiveness of Stemming Algorithms in Indonesian Documents,” in Proceedings of the 2nd Borobudur International Symposium on Science and Technology (BIS-STE 2020), 2021, vol. 203, pp. 154–158, doi: 10.2991/aer.k.210810.025.
V. R. Prasetyo, I. A. Ryanda, and Delta Ardy Prima, “SENTIMENT ANALYSIS AND CATEGORIZATION OF CUSTOMER REVIEWS ON KOPI PASTE CAFE USING NAIVE BAYES AND KNEAREST NEIGHBOR METHODS,” NERO Netw. Eng. Res. Oper., vol. 8, no. 1, 2024, doi: 10.21107/nero.v8i1.18465.
M. U. Albab, Y. Karuniawati P, and M. N. Fawaiq, “Optimization of the Stemming Technique on Text preprocessing President 3 Periods Topic,” J. Transform., vol. 20, no. 2, pp. 1–10, 2023, doi: 10.26623/transformatika.v20i2.5374.
S. Firman Sodiq, W. Desena, and A. Wibowo, “Penerapan Algoritma Stemming Nazief & Adriani Pada Proses Klasterisasi Berita Berdasarkan Tematik Pada Laman (Web) Direktorat Jenderal HAM Menggunakan Rapidminer,” Syntax J. Inform., vol. 11, no. 02, pp. 10–21, 2022, doi: 10.35706/syji.v11i02.7192.
M. Domingo, M. García-Martínez, A. Helle, F. Casacuberta, and M. Herranz, “How Much Does Tokenization Affect Neural Machine Translation?,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023, vol. 13451 LNCS, pp. 545–554, doi: 10.1007/978-3-031-24337-0_38.
A. A. Kurniawan and M. Mustikasari, “Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia,” J. Inform. Univ. Pamulang, vol. 5, no. 4, p. 544, 2021, doi: 10.32493/informatika.v5i4.6760.
F. Yao et al., “Establishment and Validation of a Liquid Chromatography-Tandem Mass Spectrometry Method for the Determination of Tigecycline in Critically Ill Patients,” Int. J. Anal. Chem., vol. 2020, 2020, doi: 10.1155/2020/6671392.
H. Wang et al., “Ecological and health risk assessments of polycyclic aromatic hydrocarbons (PAHs) in soils around a petroleum refining plant in China: A quantitative method based on the improved hybrid model,” J. Hazard. Mater., vol. 461, 2024, doi: 10.1016/j.jhazmat.2023.132476.
Y. HaCohen-Kerner, D. Miller, and Y. Yigal, “The influence of preprocessing on text classification using a bag-of-words representation,” PLoS One, vol. 15, no. 5, 2020, doi: 10.1371/journal.pone.0232525.
S. Khatai, S. S. Rautaray, S. Sahoo, and M. Pandey, “An implementation of text mining decision feedback model using hadoop mapreduce,” in Studies in Computational Intelligence, vol. 954, 2021, pp. 273–306.
M. D. Purbolaksono, F. D. Reskyadita, Adiwijaya, A. A. Suryani, and A. F. Huda, “Indonesian text classification using back propagation and sastrawi stemming analysis with information gain for selection feature,” Int. J. Adv. Sci. Eng. Inf. Technol., no. 1, pp. 234–238, 2020, doi: 10.18517/ijaseit.10.1.8858.
N. Pamungkas et al., “Comparison of Stemming Test Results of Tala Algorithms with Nazief Adriani in Abstract Documents and National News,” Inf. J. Ilm. Bid. Teknol. Inf. dan Komun., vol. 8, no. 1, pp. 33–41, 2023, doi: 10.25139/inform.v8i1.5569.
L. Xiang, “Application of an Improved TF-IDF Method in Literary Text Classification,” Adv. Multimed., vol. 2022, 2022, doi: 10.1155/2022/9285324.
X. Tang, Y. Dai, and Y. Xiang, “Feature selection based on feature interactions with application to text categorization,” Expert Syst. Appl., vol. 120, pp. 207–216, 2019, doi: 10.1016/j.eswa.2018.11.018.
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