Utilization of the AI-BERT Model for Analyzing Student Sentiments toward Campus Services at Higher Education Institutions in South Tangerang City
DOI:
10.29303/jppipa.v11i9.12663Published:
2025-09-25Downloads
Abstract
This study aims to evaluate students’ satisfaction with academic services and analyze open-ended opinions using the Artificial Intelligence Bidirectional Encoder Representations from Transformers (AI-BERT) model. The research employed a quantitative experimental method, combining a five-point Likert scale survey across seven academic service indicators and AI-BERT sentiment analysis of 150 student comments. The results indicate that the overall student satisfaction level falls into the “good” category, with a Student Satisfaction Index (SSI) of 78.53%. The highest-rated indicator was access to academic information (mean = 4.21), while the lowest was administrative service speed (mean = 3.67). Sentiment analysis revealed 60.67% positive, 36.00% negative, and 3.33% neutral opinions, highlighting the need for improvement in service speed and staff responsiveness. Evaluation of the AI-BERT model demonstrated superior performance with an accuracy of 91.3% and an F1-score of 0.913, outperforming conventional methods such as SVM and Naïve Bayes. These findings provide a basis for recommendations on developing digital-based academic service strategies and leveraging AI technology to enhance service efficiency and quality.
Keywords:
Academic services AI-BERT Sentiment analysis Student satisfactionReferences
Aithal, P. S., & Maiya, A. K. (2023). Development of a new conceptual model for improvement of the quality services of higher education institutions in academic, administrative, and research areas. International Journal of Management, Technology, and Social Sciences (IJMTS), 8(4), 260–308. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4770790
Al-Hail, M., Zguir, M. F., & Koç, M. (2023). University students’ and educators’ perceptions on the use of digital and social media platforms: A sentiment analysis and a multi-country review. Iscience, 26(8). Retrieved from https://www.cell.com/iscience/fulltext/S2589-0042(23)01399-8
Azzahra, S. A., Saefullah, A., Tafsiruddin, M., Saksana, J. C., Nurrahman, S., Tohiroh, T., Suharmanto, S., Sutaryono, S., Suhermanto, S., Jayaun, J., Putera, A. R., Nurhayati, N., Nurhidayat, M., & Salima, R. (2025). Focus Group Discussion (FGD) Mengukur Kepuasan Pengguna Portal Akademik Perguruan Tinggi Di Tangerang Selatan Dengan Metode Pieces. Community Development Journal : Jurnal Pengabdian Masyarakat, 6(1), 305–315. https://doi.org/10.31004/CDJ.V6I1.40303
Azzahra, S. A., & Wibowo, A. (2020). Analisis Sentimen Multi-Aspek Berbasis Konversi Ikon Emosi dengan Algoritme Naïve Bayes untuk Ulasan Wisata Kuliner Pada Web Tripadvisor. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(4), 737–744. https://doi.org/10.25126/JTIIK.2020731907
Bororing, J. E., & Faeruzah, F. (2020). Analisis Sentimen Layanan Akademik Menggunakan Metode Naïve Bayes Classifier Pada Komentar Mahasiswa. Informasi Interaktif, 5(3), 129–135. Retrieved from https://e-journal.janabadra.ac.id/index.php/informasiinteraktif/article/viewFile/1309/894
Bulu, A., Umar, E., & Ate, P. M. (2024). Analisis Sentimen Terhadap Sistem Informasi Akademik STIMIKOM Stella Maris Sumba Menggunakan Algoritma Naïve Bayes. Jurnal Sistem Informasi Dan Informatika, 2(1), 115–124. https://doi.org/10.47233/JISKA.V1I2.1085
Coccoli, M., Guercio, A., Maresca, P., & Stanganelli, L. (2014). Smarter universities: A vision for the fast changing digital era. Journal of Visual Languages & Computing, 25(6), 1003–1011. https://doi.org/10.1016/j.jvlc.2014.09.007
Dervenis, C., Kanakis, G., & Fitsilis, P. (2024). Sentiment analysis of student feedback: A comparative study employing lexicon and machine learning techniques. Studies in Educational Evaluation, 83, 101406. https://doi.org/10.1016/j.stueduc.2024.101406
Dormann, M., Hinz, S., & Wittmann, E. (2019). Improving school administration through information technology? How digitalisation changes the bureaucratic features of public school administration. Educational Management Administration & Leadership, 47(2), 275–290. https://doi.org/10.1177/1741143217732793
Fitri, E., Yuliani, Y., Rosyida, S., & Gata, W. (2020). Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest dan Support Vector Machine. TRANSFORMTIKA, 18(1), 71–80. Retrieved from https://pdfs.semanticscholar.org/9961/d28fec2f9076107905d8e287653097a9d552.pdf
Hikmah Febryan, P., Kusuma Negara, A., Farell Altivan Ramadhan Sistem Informasi, M., Negeri, U., Timur Jl Rungkut Madya, J., Anyar, G., Gn Anyar, K., & Timur, J. (2025). Analisis Penggunaan Ai Dalam Algoritma Sosial Media Systematic Literature Review. JATI (Jurnal Mahasiswa Teknik Informatika), 9(1), 1095–1102. https://doi.org/10.36040/JATI.V9I1.12613
Julianto, I. T., & Lindawati, L. (2022). Analisis Sentimen Terhadap Sistem Informasi Akademik Institut Teknologi Garut. Jurnal Algoritma, 19(1), 458–468. https://doi.org/10.33364/ALGORITMA/V.19-1.1112
Kasmia, N. Ben, & M’hamed, H. (2023). Digitalization of higher education: Impacts on management practices and institutional developement. A literature review. Conhecimento & Diversidade, 15(39), 56–82. https://doi.org/10.18316/rcd.v15i39.11135
Kasztelnik, K., & Kamssu, A. J. (2025). Transforming Accounting and Business Applications with AI: BERT Framework Injection into LLMs for GenAI Model Agents. Review of Business Information Systems, 25(1). Retrieved from https://journals.klalliance.org/index.php/RBIS/article/view/502
Martha, A. (2025). Metodologi Penelitian Pendidikan: Kualitatif, Kuantitatif dan Mixed Methods pada Era Digital. Sumatera Barat: Takaza Innovatix Labs.
Mas, R., Panca, R. W., Atmaja1, K., & Yustanti2, W. (2021). Analisis Sentimen Customer Review Aplikasi Ruang Guru Dengan Metode BERT (Bidirectional Encoder Representations from Transformers. Journal of Emerging Information System and Business Intelligence (JEISBI), 2(3). Retrieved from https://ejournal.unesa.ac.id/index.php/JEISBI/article/view/41567
Maulidan, M. D., Sumarlinda, S., & Sopingi, S. (2024). Development of Sentiment Analysis System of Simple Pol Application on Google Play Store Using Naive Bayes Classifier Method and BERT Prediction. Journal of Dinda : Data Science, Information Technology, and Data Analytics, 4(2), 115–122. https://doi.org/10.20895/DINDA.V4I2.1577
Mustikasari, R. (2019). Pengembangan instrumen pengukuran kepuasan mahasiswa dalam peningkatan mutu layanan perguruan tinggi. Jurnal Edukasi: Kajian Ilmu Pendidikan, 5(1), 39–60. https://doi.org/10.51836/JE.V5I1.114
Nweke, P. O. (2025). The role of digital administrative system in transforming organizational culture in higher institution. International Journal of Studies in Education, 21(1), 184–197. Retrieved from https://ijose.unn.edu.ng/wp-content/uploads/sites/224/2025/04/IJOSE-2025-021-Nweke.pdf
Putri, N. A. R., & Ardiansyah. (2023). Analisis Sentimen Terhadap Kemajuan Kecerdasan Buatan di Indonesia Menggunakan BERT dan RoBERTa. Jurnal Sains Dan Informatika, 9(2), 136–145. https://doi.org/10.34128/JSI.V9I2.649
Rahayu, I. P., Fauzi, A., & Indra, J. (2022). Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine. Jurnal Sistem Komputer Dan Informatika (JSON) Hal, 301(2). https://doi.org/10.30865/json.v4i2.5381
Rumble, G. (2000). Student support in distance education in the 21st century: Learning from service management. Distance Education, 21(2), 216–235. https://doi.org/10.1080/0158791000210202
Sri Widagdo, A., Nuresa Qodri, K., Edi Nugroho Saputro, F., Akbar Rizky, N. P., Informasi, T., Kesehatan Dan Teknologi, F., & Muhammadiyah Klaten, U. (2023). Analisis sentimen terhadap pelayanan Kesehatan berdasarkan ulasan Google Maps menggunakan BERT. JURNAL FASILKOM, 13(02), 326–333. https://doi.org/10.37859/JF.V13I02.5170
Sugiyono. (2021). Metode penelitian kuantitatif, kualitatif, dan R&D (Cetakan Ke-3). Bandung: Afabeta.
Syahrizal, H., Kualitatif Hasan Syahrizal, J. J.-J. P. D. P. K., Ms., J., Ms., A. T. R., S., & Sulthan Thaha Saifuddin Jambi, U. (2023). Jenis-Jenis Penelitian Dalam Penelitian Kuantitatif dan Kualitatif. QOSIM : Jurnal Pendidikan, Sosial & Humaniora, 1(1), 13–23. https://doi.org/10.61104/JQ.V1I1.49
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