Dashboard Analisis Sentimen Ulasan Google Maps Perguruan Tinggi di Bali Berbasis IndoBERT
DOI:
https://doi.org/10.46880/jmika.Vol10No2.pp535-542Keywords:
Sentiment Analysis, IndoBERT, Dashboard, Web Application, Google MapsAbstract
Public opinions shared on the Google Maps platform factually reflect the reputation and service quality of higher education institutions in Bali Province. However, manually analyzing thousands of reviews is time-consuming and potentially subjective. Therefore, this research focuses on developing a web-based system equipped with an interactive visual dashboard to computerize the categorization and comparison of review sentiments. The text classification process is built using the IndoBERT (Indonesian Bidirectional Encoder Representations from Transformers) deep learning architecture, which sorts opinions into positive or negative sentiments before presenting them on the web interface. Algorithm performance evaluation shows that IndoBERT is reliable, achieving an accuracy rate of 82.18% and a sensitivity (recall) of 91.90%. The modeling output is visualized dynamically through a dashboard, enabling institutions and prospective students to monitor campus sentiments more efficiently, objectively, and precisely.
References
A’la, F. Y. (2025). Optimasi Klasifikasi Sentimen Ulasan Game Berbahasa Indonesia: IndoBERT dan SMOTE untuk Menangani Ketidakseimbangan Kelas. Edumatic: Jurnal Pendidikan Informatika, 9(1), 256–265. https://doi.org/10.29408/edumatic.v9i1.29666
Atmojo, F. W., Atina, V., & Permatasari, H. (2025). Analisis Sentimen Pelanggan Pada Ulasan Google Maps Restoran Al-Ghiff Steak Menggunakan Model Indobert. Simtek: Jurnal Sistem Informasi Dan Teknik Komputer, 10(2).
Devlin, J., Chang, M.W., & Lee, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. Aclanthology, 4171–4186.
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. https://doi.org/10.47065/josh.v5i1.4379
Jazuli, A., Widowati, & Kusumaningrum, R. (2024). Optimizing Aspect-Based Sentiment Analysis Using BERT for Comprehensive Analysis of Indonesian Student Feedback. Applied Sciences, 15(1), 172. https://doi.org/10.3390/APP15010172
Koto, F., Rahimi, A., Lau, J. H., & Baldwin, T. (2020). IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP. COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference, 757–770. https://doi.org/10.18653/V1/2020.COLING-MAIN.66
Merdiansah, R., & Ali Ridha, A. (2024). Analisis Sentimen Pengguna X Indonesia Terkait Kendaraan Listrik Menggunakan IndoBERT. Jurnal Ilmu Komputer Dan Sistem Informasi (JIKOMSI, 7(1), 221–228.
Munandar, A. A., Farikhin, F., & Widodo, C. E. (2023). Sentimen Analisis Aplikasi Belajar Online Menggunakan Klasifikasi SVM. JOINTECS (Journal of Information Technology and Computer Science), 8(2), 77. https://doi.org/10.31328/JOINTECS.V8I2.4747
Musthafa, A., Muriyatmoko, D., & Lutfiansah, F. A. (2025). Implementasi Web-based Sentiment Analysis Menggunakan Metode Machine Learning. Seminar Nasional Inovasi Dan Tren Teknologi Informasi (SINATTI) 2025.
Nuryadi, D., Metandi, F., Alam Hadiwijaya, N., Zainul Rohman, M., Hartanto, S., & Syafrizal, A., (2025). Fine Tuning Indobert Untuk Analisis Sentimen Pada Ulasan Pengguna Aplikasi Tiket.com di Google Play Store. JATI (Jurnal Mahasiswa Teknik Informatika), 9(2), 3577–3583. https://doi.org/10.36040/JATI.V9I2.13204
Vaswani, A., Brain, G., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Wahyuni, E. (2024). Transformasi Data Statistik Menjadi Visual Interaktif Menggunakan Streamlit: Studi Kasus BPS Kota Mojokerto. Jurnal Kridatama Sains dan Teknologi,
Wirayudha, A., Murniyati, M., & Rosdiana, R. (2025). Analisis Sentimen Terhadap Ulasan Access by KAI Pada Google Play Store Menggunakan Metode Indobert. Portal Riset Dan Inovasi Sistem Perangkat Lunak, 3(1), 9–20. https://doi.org/10.59696/PRINSIP.V3I1.69
Yahya, F., & Ratnawati, D. (2025). Analisis Sentimen Ulasan Pengguna dari Google Maps Menggunakan Metode Long-Short Term Memory (Studi Kasus: Rumah Sakit Gatoel Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 9(4), 2548–2964.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 I Made Pradnyananda Kevin Arnawa, Putu Manik Prihatini, Yessi Aprilia Waluyo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
