Analisis Sentimen di Youtube Terhadap Kenaikan UKT Menggunakan Metode Support Vector Machine

Authors

  • Nur Aisyah Wahyuni Universitas Multi Data Palembang, Indonesia
  • Dinda Putri Ayu Universitas Multi Data Palembang, Indonesia
  • Hafidz Irsyad Universitas Multi Data Palembang, Indonesia

DOI:

https://doi.org/10.29240/arcitech.v4i1.10829

Keywords:

Analysis, Single Tuition Fee Increase, Sentiment, Support Vector Machine, YouTube

Abstract

Students and the general public usually have different responses to the increase in Single Tuition Fees (UKT) at universities. Protests, complaints, and support for this increase may be expressed through various social media platforms, such as YouTube. Using the Support Vector Machine (SVM) method, this study analyzes comments on the YouTube platform related to the increase in UKT. Comment data is divided into three categories: positive, negative, and neutral. The evaluation results show that the SVM model achieves an accuracy of 0.88; it also demonstrates good ability to recognize negative sentiment with a precision of 0.83, recall of 0.90, and f1-score of 0.86. For neutral sentiment, the model shows a precision of 0.86, recall of 0.75, and f1-score of 0.80. Nevertheless, the SVM model achieves perfect scores for precision, recall, and f1-score of 1.00 for positive sentiment. Although the SVM model has proven effective in analyzing sentiment towards the increase in UKT on YouTube, further improvements are needed to enhance accuracy in identifying neutral sentiment.

Downloads

Download data is not yet available.

Author Biographies

Nur Aisyah Wahyuni, Universitas Multi Data Palembang

Program Studi Informatika

Dinda Putri Ayu, Universitas Multi Data Palembang

Program Studi Informatika

References

F. A. Nugraha, N. H. Harani, and R. Habibi, Analisis Sentimen Pembatasan Sosial Menggunakan Deep Learning, 1st ed. Bandung: Kreatif, 2020.

N. C. Dang, M. N. Moreno-García, and F. de la Prieta, “Sentiment analysis based on deep learning: A comparative study,†Electronics (Switzerland), vol. 9, no. 3, Mar. 2020, doi:10.3390/electronics9030483

Z. Hao, L. Shaohong, and S. Jinping, “Unitmodel of binary SVM with DS output and its application in multi-class SVM,†Proc. – 2011 4th Int. Symp. Comput. Intell. Des. Isc. 2011, vol.1, no. 2, pp. 101–104, 2011, doi:10.1109/ISCID.2011.34

V. A. Fitri, R. Andreswari, and M. A. Hasibuan, “Sentiment Analysis of Social Media Twitter with Case of Anti-LGBT Campaign In Indonesia Using NaïveBayes, Decision Tree, and Random Forest Algorithm,â€Procedia Comput. Sci., Vol. 161, pp. 765–772, 2019, doi: 10.1016/j.procs.2019.11.181.

P. Y. Saputra, D. H. Subhi, and F. Z. A. 2019. Winatama, “Implementasi Sentimen Analisis Channel Video Pelayanan Pemerintah Di YouTube Menggunakan Algoritma Naïve Bayes,†J. Inform. Polinema, vol. 5, no. 4, pp.209–213, doi:10.33795/jip.v5i4.259

Qosim, Ahmad. 2021, “Perbandingan Metode Klasifikasi Support Vector Machine (SVM) dan Naïve Bayes Classifier (NBC) Untuk Menentukan Kualitas Udaraâ€, Fakultas Sains dan Teknologi, Universitas Islam Negeri Maulana Malik Ibrahim.

R. R. Khaerullah, N. Suarna, and O. Nurdiawan, “Analisa Pengelompokan Dataset Komputer Menggunakan Algoritma X-Means,†J. Inform.dan Teknol. Inf., vol. 1, no. 2, pp. 125–132, 2023,doi: 10.56854/jt.v1i2.135.

T. Prasetiya, I. Ali, C. L. Rohmat, and O.Nurdiawan, “Klasifikasi Status Stunting Balita Di Desa Slangit Menggunakan Metode K- Nearest Neighbor,†INFORMATICS Educ. Prof.J. Informatics, vol. 5, no. 1, p. 93, 2020, doi:10.51211/itbi.v5i1.1431

Syafi’i, O. Nurdiawan, and G. Dwilestari, “Penerapan Machine Learning Untuk Menentukan Kelayakan Kredit Menggunakan Metode Support Vektor Machine,†J. Sist. Inf. dan Manaj., vol. 10, no. 2, pp. 1–6, 2022

Sari, E. D. N., & Irhamah, I. (2020). Analisis Sentimen Nasabah Pada Layanan Perbankan Menggunakan Metode Regresi Logistik Biner, Na"ive Bayes Classifier (NBC), dan Support Vector Machine (SVM). Jurnal Sains Dan Seni ITS, 8(2), D177–D184.I. A. Ropikoh, R. Abdulhakim, U. Enri, and N. Sulistiyowati, “Penerapan Algoritme Support Vector Machine (SVM) untuk Klasifikasi Berita Hoax Covid-19,†2021. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC

E. Yosef, S. Sihombing, and B. T. Hanggara, “Pemanfaatan API Youtube Dalam Pengembangan Aplikasi Portal Video Penangkaran Kenari Untuk Peternak Kenari Berbasis Android,â€Vol. 4, No. 7, pp. 2067–2074, 2020.

A. N. Yusuf, E. Supriyati, and T. Listyorini.2020. “Analisis Sentimen Mengenai Layanan

Provider Indihome Berdasarkan Pendapat Pelanggan Melalui Media Sosial Twitter dengan Metode Naïve Bayes Classifier,†vol.4, pp. 75–78

Mualfah, D., Ramadhoni., Gunawan, R., Suratno, D. M.(2023).Analisis Sentimen Komentar YouTube TvOne Tentang Ustadz Abdul Somad Dideportasi Dari Singapura Menggunakan Algoritma SVM. Jurnal FASILKOM,13(1) ,72-80. https://ejurnal.umri.ac.id/index.php/JIK/article/view/4920/2592

Abdusyukur, Fatwa.(2023).PENERAPAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PENCEMARAN NAMA BAIK DI MEDIA SOSIAL TWITTER. Jurnal Ilmiah Komputer dan Informatika, 12(1), 1-10. https://ojs.unikom.ac.id/index.php/komputa/article/view/9418/3696

Ardiansyah, M. Suharto, T. Farid, Ahmad, S.(2022). “Upaya Penanganan Uang Kuliah Tunggal Bermasalah Bagi Mahasiswa Yang Tidak Mampu Pada Perguruan Tinggiâ€.,Jurnal Ilmiah Ilmu Pendidikan.

O. H. Rahman, Abdillah Gunawan & Komarudin Agus, “Klasifikasi Ujaran Kebencian pada Media

Sosial Twitter Menggunakan Support Vector Machineâ€, Jurnal Rekayasa Sistem dan Teknologi Informasi (RESTI), vol. 5, no. 1, pp. 17 – 23, 2021

Downloads

Published

30-06-2024

How to Cite

Wahyuni, N. A., Ayu, D. P., & Irsyad, H. (2024). Analisis Sentimen di Youtube Terhadap Kenaikan UKT Menggunakan Metode Support Vector Machine. Arcitech: Journal of Computer Science and Artificial Intelligence, 4(1), 57–71. https://doi.org/10.29240/arcitech.v4i1.10829

Issue

Vol. 4 No. 1 (2024): June 2024

Section

Articles

Citation Check

License

Authors who publish with Arcitech: Journal of Computer science and Artificial Intelligence agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).