Email this article 
Sentiment Analysis Of Student Opinion Related To Online Learning Using Naïve Bayes Classifier Algorithm And SVM With Adaboost On Twitter Social Media
Abstract
Twitter is one of the social media that functions to express opinions on issues or problems that are currently happening, such as problems in the social, economic, educational and other fields. One of the issues being discussed so far is online learning. The government has issued a policy, one of which is for all students to study at home online by using a network to be able to interact with each other like in the classroom. The government's reason for issuing this policy is to break the chain of the spread of the Covid-19 virus, which until now has not subsided. Regarding this online learning policy, there are pros and cons. This opinion is widely expressed on social media, one of which is Twitter. Sentiment analysis is a method for analyzing an opinion which aims to classify texts. The Naïve Bayes Classifier and Support Vector Machine methods are methods machine learning that can be used for sentiment analysis. The problem in classifying text is that the resulting accuracy is less than optimal, so feature selection or boosting is needed to improve its accuracy. In this study, optimization of boosting was carried out using Adaboost. The purpose of this study is to compare the performance of the algorithm before and after using Adaboost. The results of the sentiment analysis on online learning obtained the highest accuracy results by the Naïve Bayes Classifier algorithm coupled with Adaboost of 99.26%, with a precision of 99.39% and recall of 99.20%.
Keywords
Full Text:
PDFReferences
Eben Munthe, “Pandemi Covid-19 dan Dampaknya terhadap Angka Perceraian dan Kelahiran,” Pneum. J. Teol. Kependetaan, vol. 12, no. 2, pp. 104–115, 2022.
S. S, Putri Wulandini, “HUBUNGAN PENDIDIKAN DENGAN UPAYA PENCEGAHAN COVID-19 DI DESA LAKSAMANA KECAMATAN SABAK AUH KABUPATEN SIAK,” J. Ilmu Kesehat. Masy. (Journal Public Heal. Sci., vol. 10, 2021.
I. G. G. P. A. P. Ida Bagus Gede Paramita, “New Normal Bagi Pariwisata Bali Di Masa Pandemi Covid-19,” J. Ilm. Pariwisata Agama dan Budaya, vol. EISSN 2614, p. ISSN 2527-9734, 2020, doi: 10.36275/mws.
A. J. Rajab, M. S. Nurdin, and H. Mubarak, “Tinjauan Hukum Islam pada Edaran Pemerintah dan MUI dalam Menyikapi Wabah Covid-19,” BUSTANUL FUQAHA J. Bid. Huk. Islam, vol. 1, no. 2, pp. 156–173, 2020.
Ahmadi, “Pelaksanaan Pembelajaran Di Stai Rakha Sebelum, Semasa Dan Sesudah Pandemi Covid-19,” Adiba J. Educ., vol. 2, no. 1, pp. 51–63, 2022.
A. R. Rizkirobby, M. Nasrun, and R. A. Nugrahaeni, “Deteksi Ujaran Ancaman Berbasis Website Pada Postingan Media Sosial Twitter Menggunakan Metode Naive Bayes,” eProceedings Eng., vol. 8, no. 1, pp. 492–499, 2021.
A. Rahmatulloh, R. N. Shofa, I. Darmawan, and Ardiansah, “Sentiment Analysis of Ojek Online User Satisfaction Based on the Naïve Bayes and Net Brand Reputation Method,” 2021 9th Int. Conf. Inf. Commun. Technol. ICoICT 2021, pp. 337–341, 2021, doi: 10.1109/ICoICT52021.2021.9527466.
N. L. P. C. Savitri, R. A. Rahman, R. Venyutzky, and N. A. Rakhmawati, “Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning,” J. Tek. Inform. dan Sist. Inf., vol. 7, no. 1, pp. 47–58, 2021, doi: 10.28932/jutisi.v7i1.3216.
M. I. Ahmadi, D. Gustian, and F. Sembiring, “Analisis Sentiment Masyarakat terhadap Kasus Covid-19 pada Media Sosial Youtube dengan Metode Naive bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 807–814, 2021.
R. Slamet, W. Gata, A. Novtariany, K. Hilyati, and F. A. Jariyah, “Analisis Sentimen Twitter Terhadap Penggunaan Artis Korea Selatan Sebagai Brand Ambassador Produk Kecantikan Lokal,” INTECOMS J. Inf. Technol. Comput. Sci., vol. 5, no. 1, pp. 145–153, 2022, doi: 10.31539/intecoms.v5i1.3933.
A. Bayhaqy, S. Sfenrianto, K. Nainggolan, and E. R. Kaburuan, “Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes,” 2018 Int. Conf. Orange Technol. ICOT 2018, pp. 1–6, 2018, doi: 10.1109/ICOT.2018.8705796.
V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm,” Procedia Comput. Sci., vol. 161, pp. 765–772, 2019, doi: 10.1016/j.procs.2019.11.181.
A. M. Pravina, I. Cholissodin, and P. P. Adikara, “Analisis Sentimen Tentang Opini Maskapai Penerbangan pada Dokumen Twitter Menggunakan Algoritme Support Vector Machine (SVM),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 3, pp. 2789–2797, 2019, [Online]. Available: http://j-ptiik.ub.ac.id.
B. K. Bhavitha, A. P. Rodrigues, and N. N. Chiplunkar, “Comparative study of machine learning techniques in sentimental analysis,” Proc. Int. Conf. Inven. Commun. Comput. Technol. ICICCT 2017, no. March 2017, pp. 216–221, 2017, doi: 10.1109/ICICCT.2017.7975191.
A. Rane and A. Kumar, “Sentiment Classification System of Twitter Data for US Airline Service Analysis,” Proc. - Int. Comput. Softw. Appl. Conf., vol. 1, pp. 769–773, 2018, doi: 10.1109/COMPSAC.2018.00114.
N. Novianti, M. Zarlis, and P. Sihombing, “Penerapan Algoritma Adaboost Untuk Peningkatan Kinerja Klasifikasi Data Mining Pada Imbalance Dataset Diabetes,” J. Media Inform. Budidarma, vol. 6, no. April, pp. 1200–1206, 2022, doi: 10.30865/mib.v6i2.4017.
A. Syaifuddin et al., “Analisis Sentimen Pada Sosial Media Tentang Implementasi Kebijakan Pse Kominfo Menggunakan Algoritme Lexicon,” Pros. Semastek 2022, vol. 1, no. 1, pp. 8–15, 2022.
F. Bei and S. Sudin, “Analisis Sentimen Aplikasi Tiket Online Di Play Store Menggunakan Metode Support Vector Machine (Svm),” Sismatik, vol. 01, no. 01, pp. 91–97, 2021.
DOI: https://doi.org/10.31315/telematika.v20i2.8827
DOI (PDF): https://doi.org/10.31315/telematika.v20i2.8827.g5659
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Status Kunjungan Jurnal Telematika