Visualisasi Data Tweet di Sektor Pendidikan Tinggi Pada Saat Masa Pandemi

Djoko Cahyo Utomo Lieharyani; Rita Ambarwati

doi:10.47065/bits.v4i1.1551

Djoko Cahyo Utomo Lieharyani * Politeknik Negeri Bandung, Bandung, Indonesia http://orcid.org/0000-0003-2573-5778
Rita Ambarwati Universitas Muhammadiyah Sidoarjo, Sidoarjo, Indonesia http://orcid.org/0000-0001-8302-1170

(*) Corresponding Author

DOI: https://doi.org/10.47065/bits.v4i1.1551

Keywords: Covid19; Preprocessing; Semantics; Higher Education; Social Media

Abstract

The Covid-19 pandemic has had an impact on many sectors, including higher education. Various public opinions about higher education have emerged on social media during the pandemic, with varying foci of discussion. The purpose of this study is twofold. The first is to compare the results of semantic data analysis with and without stemming. Second, to determine the topic of discussion among Twitter users about higher education. This study was carried out by collecting tweet data for a year during the pandemic, from March 2020 to March 2021. The data then pre-processed using a combination of methods to ensure that there was minimal noise data. The analysis is performed using information theory, bigram phrases, semantic relationship analysis between words, and visualized using gephi in the final stage. The findings of this study explain in detail why the stemming method cannot be used directly for preprocessing data that requires semantic understanding. Data visualization also reveals that Twitter users who talk about higher education are more concerned with campus life and new student admissions. This research evaluates the stemming process because the results show that using stemming eliminates semantic meaning between words. This research also visualizes semantic relationships in tweets about higher education

Downloads

Download data is not yet available.

References

A. G. Ekp, F. E. B. Unsyiah, K. Darussalam, B. Aceh, P. Covid, and D. I. Indonesia, “Merespon Nalar Kebijakan Negara Dalam Menangani Pandemi Covid 19 Di Indonesia,” J. Ekon. dan Kebijak. Publik Indones., vol. 7, no. 1, pp. 36–53, 2020, doi: 10.24815/ekapi.v7i1.17370.

L. D. Herliandry, N. Nurhasanah, M. E. Suban, and H. Kuswanto, “Pembelajaran Pada Masa Pandemi Covid-19,” JTP - J. Teknol. Pendidik., vol. 22, no. 1, pp. 65–70, 2020, doi: 10.21009/jtp.v22i1.15286.

N. N. S. Witari and J. Suryana, “Persepsi Mahasiswa Vokasi Terhadap Kegiatan Pembelajaran Mata Kuliah Teori Psikologi Komunikasi Pada Era Pandemi Covid 19,” J. IKA, vol. 18, no. 2, pp. 199–214, 2020.

D. C. U. Lieharyani, P. G. A. P. Putra, R. V. H. Ginardi, and R. A. Sukmono, “Audit conformity for higher education using good university governance (Gug) principle,” Proc. Int. Conf. Ind. Eng. Oper. Manag., vol. 0, no. March, pp. 2081–2089, 2020.

D. C. U. Lieharyani, R. V. Hari Ginardi, R. Ambarwati, and M. T. Multazam, “Assessment for good university governance in higher education focus on align strategy business with it at big data era,” J. Phys. Conf. Ser., vol. 1175, no. 1, 2019, doi: 10.1088/1742-6596/1175/1/012204.

S. Amin, “Academic Service Quality Improvement Strategies in Higher Education,” J. Madaniyah, vol. 7 Nomor 2, pp. 222–236, 2017.

M. Lucky and N. Rosmadi, “Penerapan Strategi Bisnis di Masa Pandemi Covid-19,” IKRA-ITH Ekon., vol. 4, no. 1, pp. 122–127, 2021.

R. Anizir; Wahyuni, “Pengaruh Social Media Marketing Terhadap Brand Image Perguruan Tinggi Swasta Di Kota Serang,” J. Sains Manaj., vol. 3, no. 2, p. 0, 2017.

Kastolani, “Understanding The Delivery of Islamophobic Hate Speech Via Social Media In Indonesia,” Indones. J. Islam Muslim Soc., vol. 10, no. 2, pp. 247–270, 2020, doi: 10.18326/IJIMS.V10I2.247-270.

F. Anwar, “Perubahan dan Permasalahan Media Sosial,” J. Muara Ilmu Sos. Humaniora, dan Seni, vol. 1, no. 1, p. 137, 2017, doi: 10.24912/jmishumsen.v1i1.343.

A. BIN MUHAMMAD ALKATIRI, Z. NADIAH, and A. N. S. NASUTION, “Opini Publik Terhadap Penerapan New Normal Di Media Sosial Twitter,” Cover. J. Strateg. Commun., vol. 11, no. 1, pp. 19–26, 2020, doi: 10.35814/coverage.v11i1.1728.

P. Patmawati and M. Yusuf, “Analisis Topik Modelling Terhadap Penggunaan Sosial Media Twitter oleh Pejabat Negara,” Build. Informatics, Technol. Sci., vol. 3, no. 3, pp. 122–129, 2021, doi: 10.47065/bits.v3i3.1012.

F. R. Hartono, Y. A. Sari, and P. P. Adikara, “Pembangkitan Aturan Pengenalan Emosi Pada Twitter Menggunakan Metode Fuzzy-C Means,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. 10, pp. 3258–3264, 2018.

K. Sussolaikah, “Pemanfaatan Packages Pada R Programming Untuk Crawling Data Pada Social Media,” Build. Informatics, Technol. Sci., vol. 3, no. 3, pp. 203–206, 2021, doi: 10.47065/bits.v3i3.1035.

B. A. H. Murshed, H. D. E. Al-Ariki, and S. Mallappa, “Semantic analysis techniques using twitter datasets on big data: Comparative analysis study,” Comput. Syst. Sci. Eng., vol. 35, no. 6, pp. 495–512, 2020, doi: 10.32604/CSSE.2020.35.495.

M. R. Adrian, M. P. Putra, M. H. Rafialdy, and N. A. Rakhmawati, “Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB,” J. Inform. Upgris, vol. 7, no. 1, pp. 36–40, 2021, doi: 10.26877/jiu.v7i1.7099.

A. S. Aribowo and S. Khomsah, “Implementation Of Text Mining For Emotion Detection Using The Lexicon Method (Case Study: Tweets About Covid-19),” Telematika, vol. 18, no. 1, p. 49, 2021, doi: 10.31315/telematika.v18i1.4341.

M. A. Fauzi, D. C. Utomo, E. S. Pramukantoro, and B. D. Setiawan, “Automatic essay scoring system using N-GRAM and cosine similarity for gamification based elearning,” 2017, doi: 10.1145/3133264.3133303.

N. Aliyah Salsabila, Y. Ardhito Winatmoko, A. Akbar Septiandri, and A. Jamal, “Colloquial Indonesian Lexicon,” Proc. 2018 Int. Conf. Asian Lang. Process. IALP 2018, pp. 226–229, 2019, doi: 10.1109/IALP.2018.8629151.

C. J. Maley, “Continuous Neural Spikes and Information Theory,” Rev. Philos. Psychol., vol. 11, no. 3, pp. 647–667, 2020, doi: 10.1007/s13164-018-0412-5.

J. A. Danowski, B. Yan, and K. Riopelle, A semantic network approach to measuring sentiment, vol. 55, no. 1. Springer Netherlands, 2021.

J. A. Danowski and K. Riopelle, “Scaling constructs with semantic networks,” Qual. Quant., vol. 53, no. 5, pp. 2671–2683, 2019, doi: 10.1007/s11135-019-00879-5.

Z. Hou, F. Cui, Y. Meng, T. Lian, and C. Yu, “Opinion mining from online travel reviews: A comparative analysis of Chinese major OTAs using semantic association analysis,” Tour. Manag., 2019, doi: 10.1016/j.tourman.2019.03.009.

M. Koponen, M. A. Asikainen, A. Viholainen, and P. E. Hirvonen, “Using network analysis methods to investigate how future teachers conceptualize the links between the domains of teacher knowledge,” Teach. Teach. Educ., 2019, doi: 10.1016/j.tate.2018.12.010.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Visualisasi Data Tweet di Sektor Pendidikan Tinggi Pada Saat Masa Pandemi