Sentimen Analisis Media Sosial Terhadap Isu Pagar Laut Dengan Algoritma Support Vector Machine dan Logistic Regression
DOI:
https://doi.org/10.52436/1.jpti.888Keywords:
analisis sentimen, logistic regression, media sosial, support vector machineAbstract
Penelitian ini mencakup analisis sentimen pada media sosial terkait isu pagar laut menggunakan algoritma Logistic Regression (LR) dan Support Vector Machine (SVM). Data diperoleh dari media sosial Twitter, Instagram, Facebook, dan Tiktok yang dilakukan pra pemrosesan dan labelling menggunakan VADER. Hasil dari tiga rasio yang digunakan menunjukkan rasio 0,7 atau 7:3 adalah yang terbaik, dengan akurasi SVM 0.985382 dan akurasi LR 0.988881. Secara keseluruhan kedua algoritma memberikan hasil yang sama baiknya dan seimbang melihat dari evaluasi precision, recall, dan F1-score. Penelitian ini diharapkan dapat memberikan gambaran rangkuman opini publik terhadap isu pagar laut pada media sosial.
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E. R. Lidinillah, T. Rohana, and A. R. Juwita, "Analisis sentimen Twitter terhadap Steam menggunakan algoritma logistic regression dan support vector machine," TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika, vol. 10, no. 2, pp. 154–164, 2023, doi: 10.37373/tekno.v10i2.440
M. Qadri, "Pengaruh media sosial dalam membangun opini publik," Qaumiyyah: Jurnal Hukum Tata Negara, vol. 1, no. 1, pp. 49–63, 2020, doi: 10.24239/qaumiyyah.v1i1.4
L. Judijanto et al., "Pengaruh sumber informasi dan interaksi sosial di media sosial terhadap pembentukan opini politik masyarakat di Indonesia," Sanskara Ilmu Sosial dan Humaniora, vol. 1, no. 01, pp. 21–31, 2023, doi: 10.58812/sish.v1i01.303
M. Isnan, G. N. Elwirehardja, and B. Pardamean, "Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model," 8th International Conference on Computer Science and Computational Intelligence (ICCSCI 2023), 2023, doi: 10.1016/j.procs.2023.10.514
P. Arsi and R. Waluyo, "Analisis sentimen wacana pemindahan ibu kota Indonesia menggunakan algoritma Support Vector Machine (SVM)," J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, pp. 147, 2021, doi: 10.25126/jtlik.202183944
I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, "Analisis sentimen terhadap penggunaan aplikasi Shopee menggunakan algoritma Support Vector Machine (SVM)," Jambura Journal of Electrical and Electronics Engineering, vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830
B. Ramadhani, R. R. Suryono, and K. Kunci, "Komparasi Algoritma Naïve Bayes dan Logistic Regression Untuk Analisis Sentimen Metaverse," Jurnal Media Informatika Budidarma, vol. 8, pp. 714-725, 2024, doi: 10.30865/mib.v812.7458
T. A. Khan, R. Sadiq, Z. Shahid, M. M. Alam, and M. B. M. Su'ud, "Sentiment Analysis using Support Vector Machine and Random Forest," Journal of Informatics and Web Engineering, vol. 3, no. 1, February 2024, doi: 10.33093/jiwe.2024.3.1.5
C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
H. P. Singh, N. Singh, A. Mishra, S. K. Sen, M. Swarnkar and D. Pandey, "Logistic Regression based Sentiment Analysis System: Rectify," 2024 IEEE International Conference on Big Data & Machine Learning (ICBDML), Bhopal, India, 2024, pp. 186-191, doi: 10.1109/ICBDML60909.2024.10577296.
B. Kabra and C. Nagar, “Convolutional Neural Network based sentiment analysis with TF-IDF based vectorization”, J Integr Sci Technol, vol. 11, no. 3, p. 503, Jan. 2023, Accessed: Feb. 25, 2025. [Online]. Available: https://pubs.thesciencein.org/journal/index.php/jist/article/view/503
S. Styawati, N. Hendrastuty, and A. R. Isnain, "Analisis sentimen masyarakat terhadap program kartu prakerja pada twitter dengan metode support vector machine," Jurnal Informatika: Jurnal Pengembangan IT, vol. 6, no. 3, pp. 150-155, 2021, doi: 10.30591/jpit.v6i3.2870
A. Ambarwari, Q. J. Adrian, and Y. Herdiyeni, "Analysis of the effect of data scaling on the performance of the machine learning algorithm for plant identification," Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 1, pp. 117-122, 2020, doi: 10.29207/resti.v4i1.1517
Z. Liu, "A method of SVM with normalization in intrusion detection," Procedia Environmental Sciences, vol. 11, pp. 256-262, 2011, doi: 10.1016/j.proenv.2011.12.040
M. Sharma, S. K. Agarwal, and M. Bundele, "Decisive Analysis of multiple logistic regression apropos of hyper-parameters," Indian J. Comput. Sci. Eng., vol. 13, pp. 188-196, 2022, doi: 10.21817/indjcse/2022/v13i1/221301190
G. I. Diaz, A. Fokoue-Nkoutche, G. Nannicini, and H. Samulowitz, "An effective algorithm for hyperparameter optimization of neural networks," *IBM Journal of Research and Development*, vol. 61, no. 4/5, pp. 9:1-9:11, 2017, doi: 10.1147/JRD.2017.2709578
D. M. Cristea, I. Sima, and L. B. Iantovics, "How Good Perform Logistic Regression Algorithm for Complex Gastroenterological Image Analysis. Comparativ Analysis with Physicians Performace," 2024, doi: 10.20944/preprints202410.0683.v1
Aulia, T. M. P., Arifin, N., and Mayasari, R., “Perbandingan Kernel Support Vector Machine (SVM) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19,” SINTECH Journal, vol. 4, no. 2, pp. 139-145, Oct. 2021, doi: 10.31598/sintechjournal.v4i2.762
Syah, H. and Witanti, A., "Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (SVM)," Jurnal Sistem Informasi Dan Informatika (SIMIKA), vol. 5, no. 1, pp. 59-67, Apr. 2022, doi: 10.47080/simika.v5i1.1411
