Implementasi Algoritma SVM Non-Linear Pada Klasifikasi Analisis Sentimen Perkembangan AI di Sektor Pendidikan
- Alda Nabila Putri Politeknik Negeri Sriwijaya, Indonesia
-
Aryanti Aryanti
*
Politeknik Negeri Sriwijaya,
Indonesia
- Sopian Soim Politeknik Negeri Sriwijaya, Indonesia (*) Corresponding Author
Abstract
As technology advances, the utilization of the X platform or formerly Twitter is expanding, allowing users to exchange opinions on various topics including the transformative impact of AI in the Education sector. While AI has great potential in revolutionizing the quality and accessibility of education, it can also bring potential challenges, such as over-reliance on technology. Sentiment analysis is a computational approach to identify, extract, and classify sentiments, opinions, and emotions expressed in text. To examine the problem, this research implements a Non-Linear Support Vector Machine model to analyze sentiment about AI in the education sector. This study built four SVM models with different kernel functions, namely linear, RBF, Polynomial, and Sigmoid kernels. By utilizing 3,000 tweet data collected from platform X by scraping technique, the SVM model with polynomial kernel succeeded in becoming the best model, with accuracy, precision, recall and f1-score values of 92%. This model was able to classify 52.9% of the tweet data with positive sentiment and 47.1% of the tweet data with negative sentiment, which shows that in general, users of platform X tend to have a positive sentiment towards the development of AI in the education sector.
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References
L. Gkinko and A. Elbanna, “The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users,” Int J Inf Manage, vol. 69, Apr. 2023, doi: 10.1016/j.ijinfomgt.2022.102568.
S. Liza Zahara, Z. Ula Azkia, and M. Minan Chusni Program Studi Pendidikan Fisika UIN Sunan Gunung Djati Bandung, “Implementasi Teknologi Artificial Intelligence (AI) dalam Bidang Pendidikan,” JPSP: Jurnal Penelitian Sains dan Pendidikan, vol. 3, 2023.
G. Yulianti, Benardi, N. Permana, and F. A. K. Wijayanti, “Transformasi Pendidikan Indonesia:Menerapkan Potensi Kecerdasan Buatan (AI),” JISMA: Journal of Information Systems and Management, vol. 2, no. 6, Des. 2023.
M. I. Fikri, T. S. Sabrila, Y. Azhar, and U. M. Malang, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” SMATIKA, vol. 10, no. 2, Des. 2020.
T. W. Putra, A. Triayudi, and Andrianingsih, “View of Analisis Sentimen Pembelajaran Daring menggunakan Metode Naïve Bayes, KNN, dan Decision Tree,” JTIK: Jurnal Teknologi Informasi dan Komunikasi, vol. 6, no. 1, 2022.
M. Khoirul, U. Hayati, and O. Nurdiawan, “Analisis Sentimen Aplikasi Brimo Pada Ulasan Pengguna di Google Play Menggunakan Algoritma Naive Bayes,” JATI: Jurnal Mahasiswa Teknik Informatika, vol. 7, no. 1, Feb. 2023.
R. Obiedat et al., “Sentiment Analysis of Customers’ Reviews Using a Hybrid Evolutionary SVM-Based Approach in an Imbalanced Data Distribution,” IEEE Access, vol. 10, pp. 22260–22273, 2022, doi: 10.1109/ACCESS.2022.3149482.
Q. A. Chairunnisa, Y. Herdiyeni, M. Kusuma, D. Hardhienata, and J. Adisantoso, “Analisis Sentimen Pengguna Twitter Terhadap Program Vaksinasi Covid-19 di Indonesia Menggunakan Algoritme Support Vector Machine Sentiment Analysis of Twitter Users on COVID-19 Vaccination Program in Indonesia using Support Vector Machine Algorithm,” JIKA: Jurnal Ilmu Komputer Agri-Informatika, vol. 9, no. 1, 2022.
M. Azimi-Pour, H. Eskandari-Naddaf, and A. Pakzad, “Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete,” Constr Build Mater, vol. 230, Jan. 2020, doi: 10.1016/j.conbuildmat.2019.117021.
F. Nie, W. Zhu, and X. Li, “Decision Tree SVM: An extension of linear SVM for non-linear classification,” Neurocomputing, vol. 401, pp. 153–159, Aug. 2020, doi: 10.1016/j.neucom.2019.10.051.
A. Al Kaafi, H. Rachmi, and T. Informasi, “Algoritma Klasifikasi pada Analisis Sentimen Klaim Manfaat Jaminan Hari Tua Comparison of Classification Algorithms toward Sentiment Analysis on Claim of Old Age Guaranteed,” SISTEMASI: Jurnal Sistem Informasi Komparasi, vol. 12, no. 1, Jan. 2023.
D. Atmajaya, A. Febrianti, H. Darwis, “View of Metode SVM dan Naive Bayes untuk Analisis Sentimen ChatGPT di Twitter,” IJCS: Indonesian Journal of Computer Science, vol. 12, no. 4, 2023.
V. K. S. Que, A. Iriani, and H. D. Purnomo, “Analisis Sentimen Transportasi Online Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization (Online Transportation Sentiment Analysis Using Support Vector Machine Based on Particle Swarm Optimization),” Jurnal Nasional Teknik Elektro dan Teknik Informasi, vol. 9, no. 2, Mei 2020.
S. Rabbani, D. Safitri, N. Rahmadhani, A. A. F. Sani, and M. K. Anam, “Perbandingan Evaluasi Kernel SVM untuk Klasifikasi Sentimen dalam Analisis Kenaikan Harga BBM,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 153–160, Oct. 2023, doi: 10.57152/malcom.v3i2.897.
R. Wati and S. Ernawati, “Analisis Sentimen Persepsi Publik Mengenai PPKM Pada Twitter Berbasis SVM Menggunakan Python,” JTIUST: Jurnal Teknik Informatika Unika St. Thomas, vol. 6, no. 2, Des. 2021
T. S. Sabrila, V. R. Sari, and A. E. Minarno, “Analisis Sentimen Pada Tweet Tentang Penanganan Covid-19 Menggunakan Word Embedding Pada Algoritma Support Vector Machine Dan K-Nearest Neighbor,” Fountain of Informatics Journal, vol. 6, no. 2, p. 69, Jul. 2021, doi: 10.21111/fij.v6i2.5536.
T. Meisya, P. Aulia, N. Arifin, and R. Mayasari, “Perbandingan Kernel Support Vector Machine (SVM) Dalam Penerapan Analisis Sentimen Vaksinisasi Covid-19”, SINTECH: Science And Information Technology, vol. 4, no. 2, Okt. 2021, doi: https://doi.org/10.31598.
H. C. Husada and A. S. Paramita, “Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM),” Teknika, vol. 10, no. 1, pp. 18–26, Feb. 2021, doi: 10.34148/teknika.v10i1.311.
T. Safitri, Y. Umaidah, and I. Maulana, “Analisis Sentimen Pengguna Twitter Terhadap BTS Menggunakan Algoritma Support Vector Machine,” JAIC: Journal of Applied Informatics and Computing, vol. 7, no. 1, Jul. 2023.
N. Fitriyah, B. Warsito, D. Asih, and I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (SVM),” JURNAL GAUSSIAN, vol. 9, no. 3, pp. 376–390, 2020.
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