Penerapan SVM dan Regresi untuk Prediksi Intensitas Sentimen Pemilu Presiden Indonesia
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R. Zaiter, N. Sabbagh, and M. Koabaz, “The Impact of Social Media on Political Efficacy and Real-Life Netizens Political Participation (Lebanon- Case Study),” International Journal of Professional Business Review, vol. 8, no. 5, p. e02153, May 2023, doi: 10.26668/businessreview/2023.v8i5.2153.
H. H. Guedea-Noriega and F. García-Sánchez, “Integroly: Automatic Knowledge Graph Population from Social Big Data in the Political Marketing Domain,” Applied Sciences (Switzerland), vol. 12, no. 16, Aug. 2022, doi: 10.3390/app12168116.
S. Berg, T. König, and A. K. Koster, “Political opinion formation as epistemic practice: the hashtag assemblage of #metwo,” Media Commun, vol. 8, no. 4, pp. 84–95, 2020, doi: 10.17645/mac.v8i4.3164.
W. van Atteveldt, M. A. C. G. van der Velden, and M. Boukes, “The Validity of Sentiment Analysis: Comparing Manual Annotation, Crowd-Coding, Dictionary Approaches, and Machine Learning Algorithms,” Commun Methods Meas, vol. 15, no. 2, pp. 121–140, 2021, doi: 10.1080/19312458.2020.1869198.
A. Yenkikar and C. N. Babu, “SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis,” Indonesian Journal of Electrical Engineering and Informatics, vol. 11, no. 1, pp. 318–336, Mar. 2023, doi: 10.52549/ijeei.v11i1.4381.
A. A. Firdaus, A. Yudhana, I. Riadi, and Mahsun, “Indonesian presidential election sentiment: Dataset of response public before 2024,” Data Brief, vol. 52, Feb. 2024, doi: 10.1016/j.dib.2023.109993.
N. K. Nissa and E. Yulianti, “Multi-label text classification of Indonesian customer reviews using bidirectional encoder representations from transformers language model,” International Journal of Electrical and Computer Engineering, vol. 13, no. 5, pp. 5641–5652, Oct. 2023, doi: 10.11591/ijece.v13i5.pp5641-5652.
H. Murfi, F. L. Siagian, and Y. Satria, “Topic features for machine learning-based sentiment analysis in Indonesian tweets,” International Journal of Intelligent Computing and Cybernetics, vol. 12, no. 1, pp. 70–81, Feb. 2019, doi: 10.1108/IJICC-04-2018-0057.
M. N. Habibi and Sunjana, “Analysis of Indonesia Politics Polarization before 2019 President Election Using Sentiment Analysis and Social Network Analysis,” International Journal of Modern Education and Computer Science, vol. 11, no. 11, pp. 22–30, Nov. 2019, doi: 10.5815/ijmecs.2019.11.04.
M. C. Galgoczy, A. Phatak, D. Vinson, V. K. Mago, and P. J. Giabbanelli, “(Re) shaping online narratives: when bots promote the message of President Trump during his first impeachment,” PeerJ Comput Sci, vol. 8, p. e947, 2022.
S. E. Bestvater and B. L. Monroe, “Sentiment is not stance: Target-aware opinion classification for political text analysis,” Political Analysis, vol. 31, no. 2, pp. 235–256, 2023.
C. Chang and X. Wang, “Research on Dynamic Political Sentiment Polarity analysis of specific group Twitter based on Deep learning method,” in Journal of Physics: Conference Series, 2020, p. 12108.
O. Olabanjo et al., “From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election,” Heliyon, vol. 9, no. 5, 2023.
A. AlDayel and W. Magdy, “Stance detection on social media: State of the art and trends,” Inf Process Manag, vol. 58, no. 4, p. 102597, 2021.
H. N. Chaudhry et al., “Sentiment analysis of before and after elections: Twitter data of US election 2020,” Electronics (Basel), vol. 10, no. 17, p. 2082, 2021.
R. Liu, X. Yao, C. Guo, and X. Wei, “Can we forecast presidential election using twitter data? an integrative modelling approach,” Ann GIS, vol. 27, no. 1, pp. 43–56, 2021.
D. Grimaldi, J. D. Cely, and H. Arboleda, “Inferring the votes in a new political landscape: The case of the 2019 Spanish Presidential elections,” J Big Data, vol. 7, no. 1, p. 58, 2020.
F. Rustam, I. Ashraf, A. Mehmood, S. Ullah, and G. S. Choi, “Tweets classification on the base of sentiments for US airline companies,” Entropy, vol. 21, no. 11, p. 1078, 2019.
A. S. Talaat, “Sentiment analysis classification system using hybrid BERT models,” J Big Data, vol. 10, no. 1, p. 110, 2023.
J. Zhao and Y. Sheng, “Uncertain support vector machine based on uncertain set theory,” Journal of Intelligent & Fuzzy Systems, no. Preprint, pp. 1–12, 2023.
M. Khatun and S. Siddiqui, “Estimating Conditional Event Probabilities with Mixed Regressors: a Weighted Nearest Neighbour Approach.,” Statistika: Statistics & Economy Journal, vol. 103, no. 2, 2023.
L. Yuningsih, G. A. Pradipta, D. Hermawan, P. D. W. Ayu, D. P. Hostiadi, and R. R. Huizen, “IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification,” Emerging Science Journal, vol. 7, no. 5, pp. 1501–1516, Oct. 2023, doi: 10.28991/ESJ-2023-07-05-04.
S. Esfahanian and E. Lee, “A novel packaging evaluation method using sentiment analysis of customer reviews,” Packaging Technology and Science, vol. 35, no. 12, pp. 903–911, Dec. 2022, doi: 10.1002/pts.2686.
K. Takahashi, K. Yamamoto, A. Kuchiba, and T. Koyama, “Confidence interval for micro-averaged F 1 and macro-averaged F 1 scores,” Applied Intelligence, vol. 52, no. 5, pp. 4961–4972, 2022.
A. Fleerackers, L. Nehring, L. A. Maggio, A. Enkhbayar, L. Moorhead, and J. P. Alperin, “Identifying science in the news: An assessment of the precision and recall of Altmetric. com news mention data,” Scientometrics, vol. 127, no. 11, pp. 6109–6123, 2022.
M. Sitarz, “Extending F1 metric, probabilistic approach,” arXiv preprint arXiv:2210.11997, 2022.
S. Sumukha and others, “Analysis of Traffic Accident Features and Crash Severity Prediction,” International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), vol. 15, no. 4, pp. 1–18, 2021.
D. Chicco, M. J. Warrens, and G. Jurman, “The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation,” PeerJ Comput Sci, vol. 7, p. e623, 2021.
T. O. Hodson, “Root mean square error (RMSE) or mean absolute error (MAE): When to use them or not,” Geoscientific Model Development Discussions, vol. 2022, pp. 1–10, 2022.
I. C. Kaplan et al., “Management strategy evaluation: allowing the light on the hill to illuminate more than one species,” Front Mar Sci, vol. 8, p. 624355, 2021.
A. R. Srinivasan et al., “Beyond RMSE: Do machine-learned models of road user interaction produce human-like behavior?,” IEEE Transactions on Intelligent Transportation Systems, 2023.
A. A. Firdaus, A. Yudhana, I. Riadi, and Mahsun, “Indonesian presidential election sentiment: Dataset of response public before 2024,” Data Brief, vol. 52, Feb. 2024, doi: 10.1016/j.dib.2023.109993.
J. J. E. Macrohon, C. N. Villavicencio, X. A. Inbaraj, and J.-H. Jeng, “A semi-supervised approach to sentiment analysis of tweets during the 2022 Philippine presidential election,” Information, vol. 13, no. 10, p. 484, 2022.
F. Said and L. Parningotan Manik, “Aspect-Based Sentiment Analysis on Indonesian Presidential Election Using Deep Learning,” Paradigma, vol. 24, no. 2, pp. 160–167, 2022, doi: 10.31294/p.v24i2.1415.
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,” Jurnal Teknik Informatika (Jutif), vol. 2, no. 2, pp. 101–107, Mar. 2021, doi: 10.20884/1.jutif.2021.2.2.51.
DOI: http://dx.doi.org/10.22441/incomtech.v15i3.28525

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