Analisis Sentimen Tanggapan Pengguna Media Sosial X Terhadap Program Beasiswa KIP-Kuliah dengan Menggunakan Algoritma Support Vector Machine (SVM)
Original Full-Text Article
Download published version for reading and archivingAbstract
The KIP-Kuliah Scholarship is an Indonesian government program which aims to provide access to higher education for students from underprivileged families. This program has become a hot topic of discussion on social media, including social media. The object of research is comments on X's social media regarding the KIP-College Scholarship. Research methods include crawling data using google collabs, data preprocessing, Support Vector Machine model training, and model evaluation using RapidMiner. The research results show that the Support Vector Machine model is able to classify sentiment with an accuracy of 86.27%, but there is a bias towards negative sentiment. The majority of public responses are negative, often regarding misuse of scholarships. The suggestions given include collecting more balanced data, using dataset balancing techniques, implementing more complex models, and more in-depth evaluation to improve model performance. It is hoped that this research will provide input for the government in improving the KIP-College Scholarship distribution mechanism so that it is more targeted and reduces the potential for abuse.
Author Biographies
Program Studi Sistem Informasi, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Program Studi Teknik Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
How to Cite
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .
- Share: You are free to copy, distribute, and transmit the work in any medium or format.
- Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
References
Total: 12 References- Akbar, Y., & Ihsan, A. N. (2023). Analisis Sentimen Twitter Terhadap Opini Masyarakat Pada Sea Games Kamboja 2023 Menggunakan Algoritma Support Vector Machine. INTECOMS: Journal of Information Technology and Computer Science, 6(2), 814-821. DOI: https://doi.org/10.31539/intecoms.v6i2.7670.
- Bagaskoro, S. A., Hasanah, A., Bahri, S., Utami, E., & Yaqin, A. (2023). ANALISIS SENTIMEN LPDP (LEMBAGA PENGELOLA DANA PENDIDIKAN) PADA MEDIA SOSIAL TWITTER. Pseudocode, 10(2), 65-73. DOI: https://doi.org/10.33369/pseudocode.10.2.65-73.
- Handayani, A., & Zufria, I. (2023). Analisis sentimen terhadap bakal capres ri 2024 di twitter menggunakan algoritma svm. Journal of Information System Research (JOSH), 5(1), 53-63. DOI: https://doi.org/10.47065/josh.v5i1.4379.
- Husada, H. C., & Paramita, A. S. (2021). Analisis Sentimen Pada Maskapai Penerbangan di Platform Twitter Menggunakan Algoritma Support Vector Machine (SVM). Teknika, 10(1), 18-26. DOI: https://doi.org/10.34148/teknika.v10i1.311.
- Larasati, A. D., Dinda, D., Aidah, N. A., Gustiputri, R., & Isyak, S. N. R. (2022). Analisis Kebijakan Program Beasiswa Kartu Indonesia Pintar-Kuliah (Kip-K) Di Universitas Diponegoro. Jurnal Ilmu Administrasi Dan Studi Kebijakan (JIASK), 5(1), 1-22. DOI: https://doi.org/10.48093/jiask.v5i1.91.
Similar Articles
Articles sharing related keywords and machine learning classifications:
Most read articles by the same author(s)
Other papers published by author(s) in this journal: