Analysis of Student Sentiment towards the Gratispol Program Using Support Vector Machine (SVM) and TF-IDF Algorithms



Metrics Analysis (Dimensions & PlumX)
Indexing:
Similarity:
© 2026 Kristian Vandi Hermawan, Heny Pratiwi, Ahmad Fahrijal Pukeng

Public policy in the education sector requires continuous evaluation to ensure the effectiveness of its implementation, one example being the Gratispol Program organized by the Provincial Government of East Kalimantan. Student opinions on this program are widely expressed through Instagram comment sections. However, the unstructured nature of the data and the presence of informal language make manual analysis difficult. This study aims to analyze student sentiment based on 539 comments from 21 posts on the official Instagram accounts of 14 higher education institutions in East Kalimantan. The data were initially labeled using IndoBERT, manually validated, extracted using TF-IDF, balanced using SMOTE, and then classified using a linear-kernel SVM algorithm optimized through GridSearchCV. The results show that neutral sentiment dominates (50.5%), followed by negative (28.9%) and positive (20.6%) sentiment. Parameter optimization increased the model's accuracy from 62.96% to 67.90%. Negative comments were dominated by complaints about delays in fund disbursement, while positive comments contained appreciation for the program. These findings provide an empirical picture of student perceptions that can serve as input for the Provincial Government of East Kalimantan in improving the implementation of the Gratispol Program.

Abdillah, F. (2024). Peran perguruan tinggi dalam meningkatkan kualitas sumber daya manusia di Indonesia. Educazione: Jurnal Multidisiplin, 1(1), 13–24.

Ahmad, F. (2024). Model implementasi kebijakan publik. INNOVATIVE: Journal of Social Science Research, 4(3), 17929–17938.

Andhini, A., Handayani, F. N., Diasih, I., & Nurmalitasari. (2025). Analisis sentimen opini publik pada channel YouTube Mata Najwa menggunakan metode SVM. JUTITI, 5(2), 139–154. https://doi.org/10.55606/jutiti.v5i2.5426

Br Sembiring, A. B. S., Robet, R., & Hoki, L. (2026). Comparison of IndoBERT and SVM performance in sentiment analysis of digital education platforms. Sinkron. https://doi.org/10.33395/sinkron.v10i1.15472

Chawla, N. V., Bowyer, K. W., Hall, L., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research. https://doi.org/10.1613/jair.953

Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press. https://doi.org/10.1017/cbo9780511801389

Dinas Komunikasi dan Informatika Provinsi Kalimantan Timur. (2026). Gratispol cair Rp288 miliar, 63 ribu mahasiswa Kaltim terima manfaat. Retrieved July 16, 2026, from https://diskominfo.kaltimprov.go.id/berita/gratispol-cair-rp288-miliar-63-ribu-mahasiswa-kaltim-terima-manfaat

Firmansyah, M. A., Saeppani, A., & Fadil, I. (2025). Analisis sentimen publik program makan bergizi gratis menggunakan Support Vector Machine. JPNM Jurnal Pustaka Nusantara Multidisiplin. https://doi.org/10.59945/jpnm.v3i4.805

Fitriyana, V., Hakim, L., Novitasari, D. C. R., & Asyhar, A. H. (2023). Analisis sentimen ulasan aplikasi Jamsostek Mobile menggunakan metode Support Vector Machine. Jurnal Buana Informatika. https://doi.org/10.24002/jbi.v14i01.6909

Idris, I. S. K., Mustofa, Y. A., & Salihi, I. (2023). Analisis sentimen terhadap penggunaan aplikasi Shopee mengunakan algoritma Support Vector Machine (SVM). Jambura Journal of Electrical and Electronics Engineering. https://doi.org/10.37905/jjeee.v5i1.16830

Joachims, T. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. Lecture Notes in Computer Science. https://doi.org/10.1007/bfb0026683

Khairani, U., Mutiawani, V., & Ahmadian, H. (2024). Pengaruh tahapan preprocessing terhadap model Indobert dan Indobertweet untuk mendeteksi emosi pada komentar akun berita Instagram. Jurnal Teknologi Informasi dan Ilmu Komputer. https://doi.org/10.25126/jtiik.1148315

Khushi, M., Shaukat, K., Alam, T. M., Hameed, I. A., Uddin, S., & Luo, S. (2021). A comparative performance analysis of data resampling methods on imbalance medical data. IEEE Access. https://doi.org/10.1109/access.2021.3102399

Liu, B. (2012). Sentiment analysis and opinion mining.

Lubis, S. K., Dar, M. H., & Nasution, F. A. (2024). Analisis sentimen ulasan pengguna aplikasi pada Google Play Store menggunakan algoritma Support Vector Machine. Informatika. https://doi.org/10.36987/informatika.v11i2.5860

Mee, A., Homapour, E., Chiclana, F., & Engel, O. (2021). Sentiment analysis using TF–IDF weighting of UK MPs’ tweets on Brexit. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2021.107238

Muzayyanah, A. B., Pawening, R. E., & Arifin, Z. (2024). Analisis sentimen pada ulasan aplikasi Ehadrah di Google Playstore menggunakan Support Vector Machine (SVM). IDEALIS: Indonesia Journal Information System, 7(2), 258-266. https://doi.org/10.36080/idealis.v7i2.3250

Naseem, U., Razzak, I., Musial, K., & Imran, M. (2020). Transformer based deep intelligent contextual embedding for twitter sentiment analysis. Future Generation Computer Systems, 113, 58-69. https://doi.org/10.1016/j.future.2020.06.050

Pemerintah Provinsi Kalimantan Timur. (2025). Peraturan Gubernur Provinsi Kalimantan Timur Nomor 24 Tahun 2025 tentang Bantuan Biaya Pendidikan bagi Mahasiswa pada Perguruan Tinggi. Samarinda: Pemerintah Provinsi Kalimantan Timur. Retrieved 17 Juli 2026 from https://jdih.kaltimprov.go.id/storage/peraturan/PERGUB_24_2025_(1).pdf

Permana, T. D., Pratama, Y. B., Wahyuzi, Z., Altiarika, E., & Pramudyantoro, A. (2025). Perbandingan performa algoritma Naive Bayes dan SVM untuk analisis sentimen komentar YouTube terhadap industri esports di Indonesia. Jurnal Ilmiah Nusantara, 2(6), 1391-1399. https://doi.org/10.61722/jinu.v2i6.6753

Ramadhan, H. S., Akbar, A. S., Sinaga, K. Y., Muthoharoh, L., Satria, A., & Manullang, M. C. (2026). Sentiment analysis of AI adoption in Indonesian higher education using machine learning and transformer-based models. arXiv preprint arXiv:2604.27439. https://doi.org/10.48550/arXiv.2604.27439

Ranjan, S., & Mishra, S. (2022). Perceiving university students' opinions from Google app reviews. Concurrency and Computation: Practice and Experience, 34(10), e6800. https://doi.org/10.1002/cpe.6800

Saputra, T. A., & Devega, M. (2025). Analisis sentimen cyberbullying pada komentar Instagram menggunakan metode klasifikasi Support Vector Machine (SVM). ZONAsi: Jurnal Sistem Informasi, 7(1), 26-36. https://doi.org/10.31849/zn.v7i1.23122

Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023). Sentiment analysis and opinion mining on educational data: A survey. Natural Language Processing Journal, 2, 100003. https://doi.org/10.1016/j.nlp.2022.100003

Sinulingga, J. E. B., & Sitorus, H. C. K. (2024). Analisis sentimen opini masyarakat terhadap film horor Indonesia menggunakan metode SVM dan TF-IDF. Jurnal Manajemen Informatika (JAMIKA), 14(1), 42-53. https://doi.org/10.34010/jamika.v14i1.11946

Verma, S. (2022). Sentiment analysis of public services for smart society: Literature review and future research directions. Government Information Quarterly, 39(3), 101708. https://doi.org/10.1016/j.giq.2022.101708

Wainer, J., & Fonseca, P. (2021). How to tune the RBF SVM hyperparameters? An empirical evaluation of 18 search algorithms. Artificial Intelligence Review, 54(6), 4771-4797. https://doi.org/10.1007/s10462-021-10011-5

Widayani, N. H., & Arriyanti, E. (2026). Analisis sentimen masyarakat terhadap program gratis pol di TikTok menggunakan algoritma Naive bayes. Bulletin of Information Technology (BIT), 7(2), 162-171. https://doi.org/10.47065/bit.v7i2.2701

Widayanti, R., & Kasih, F. C. (2026). Comparative analysis of baseline IndoBERT, class-weighted IndoBERT, and SMOTE with Support Vector Machine for handling imbalanced sentiment classification in Indonesian. Jurnal Teknik Informatika (Jutif), 7(3), 2857-2875. https://doi.org/10.52436/1.jutif.2026.7.3.5692

Yolanda, A. M., & Mulya, R. T. (2024). Implementasi metode Support Vector Machine untuk analisis sentimen pada ulasan aplikasi Sayurbox di Google Play Store. VARIANSI: Journal of Statistics and Its application on Teaching and Research, 6(2), 76-83. https://doi.org/10.35580/variansiunm258

dataset hasil preprocessing sentimen

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

The copyright is reserved to Research in Education, Technology, and Multiculture that is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Address:

CV. IMRECS: Jalan Gagak I, Kecamatan Larangan, Kota Tangerang. Postal Code: 15155

STKIP Al Islam Tunas Bangsa: Jalan Z.A. Pagar Alam No. 41 Gedong Meneng Bandar Lampung