Evaluasi Persepsi Pengguna SATUSEHAT Mobile Melalui Analisis Sentimen dan Topik Modeling Berbasis Machine Learning dan Transformer

Rizal Wahyu Pratama, Khulika Malkan, Mikhael Setia Budi, Ardelia Rachma Laksita, Habibah Ratna Dadhila Islami Hana

Abstract


Platform SATUSEHAT resmi diluncurkan Kementerian Kesehatan RI pada 26 Juli 2022 sebagai sistem integrasi data rekam medis nasional, dan pada 1 Maret 2023 PeduliLindungi bertransformasi menjadi SATUSEHAT Mobile untuk digunakan masyarakat umum. Hingga saat ini angka unduhan aplikasi melampaui 50 juta dengan rating 3.4 dari 5 bintang di Google Play Store pada 6 Juni 2026. Penelitian ini mengevaluasi persepsi 16.950 pengguna melalui klasifikasi sentimen dan identifikasi topik keluhan. Enam model diuji secara komparatif, di mana tiga pendekatan berbasis TF-IDF dengan algoritma Complement Naive Bayes, Support Vector Machine, dan Logistic Regression, serta tiga variasi IndoBERT meliputi ekstraksi fitur dengan SVM, ekstraksi fitur dengan Logistic Regression berparameter C=0,01, dan fine-tuning penuh dengan classification head. Topic modeling menggunakan Latent Dirichlet Allocation dengan delapan topik yang ditentukan berdasarkan Coherence Score tertinggi 0,5487. IndoBERT Fine-tuned mencapai F1-Macro 0,8736 dan akurasi 0,9339, melampaui baseline. Keluhan dominan adalah kegagalan aplikasi setelah pembaruan aplikasi, yaitu sebesar 23,3 persen dari total ulasan negatif, disusul penilaian aplikasi yang menyusahkan sebesar 17,6 persen dan kegagalan pendaftaran akun sebesar 17,3 persen.

Keywords


Analisis Sentimen; IndoBERT; IndoBERT Fine-tuned; SATUSEHAT Mobile; Transformasi Kesehatan

Full Text:

PDF

References


Muhammad Alfarizi dan Ngatindriatun, “Digital Health 5.0 for Health Equity in Rural Developing Regions: A Bibliometric and Systematic Review,” Mar 2026. [Daring]. Tersedia pada: https://journal.unesa.ac.id/index.php/jorris

Biro Komunikasi dan Pelayanan Masyarakat Kementerian Kesehatan RI, “Kemenkes Luncurkan Platform SATUSEHAT Untuk Integrasikan Data Kesehatan Nasional,” Jakarta, Jul 2022. Diakses: 7 Juni 2026. [Daring]. Tersedia pada: https://kemkes.go.id/id/kemenkes-ri-resmi-luncurkan-platform-integrasi-data-layanan-kesehatan-bernama-satusehat

M. Hadwan, M. Al-Sarem, F. Saeed, dan M. A. Al-Hagery, “An Improved Sentiment Classification Approach for Measuring User Satisfaction toward Governmental Services’ Mobile Apps Using Machine Learning Methods with Feature Engineering and SMOTE Technique,” Applied Sciences, vol. 12, no. 11, hlm. 5547, Mei 2022, doi: 10.3390/app12115547.

Y. Zhai, X. Song, Y. Chen, dan W. Lu, “A Study of Mobile Medical App User Satisfaction Incorporating Theme Analysis and Review Sentiment Tendencies,” Int. J. Environ. Res. Public Health, vol. 19, no. 12, hlm. 7466, Jun 2022, doi: 10.3390/ijerph19127466.

E. L. Funnell, B. Spadaro, N. Martin-Key, T. Metcalfe, dan S. Bahn, “mHealth Solutions for Mental Health Screening and Diagnosis: A Review of App User Perspectives Using Sentiment and Thematic Analysis,” Front. Psychiatry, vol. 13, Apr 2022, doi: 10.3389/fpsyt.2022.857304.

Y. Shan, M. Ji, W. Xie, K.-Y. Lam, dan C.-Y. Chow, “Public Trust in Artificial Intelligence Applications in Mental Health Care: Topic Modeling Analysis,” JMIR Hum. Factors, vol. 9, no. 4, hlm. e38799, Des 2022, doi: 10.2196/38799.

H. Imaduddin, F. Y. A’la, dan Y. S. Nugroho, “Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 8, 2023, doi: 10.14569/IJACSA.2023.0140813.

F. Koto, A. Rahimi, J. H. Lau, dan T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” dalam Proceedings of the 28th International Conference on Computational Linguistics, Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020, hlm. 757–770. doi: 10.18653/v1/2020.coling-main.66.

H. Murfi, Syamsyuriani, T. Gowandi, G. Ardaneswari, dan S. Nurrohmah, “BERT-based combination of convolutional and recurrent neural network for indonesian sentiment analysis,” Appl. Soft Comput., vol. 151, hlm. 111112, Jan 2024, doi: 10.1016/j.asoc.2023.111112.

S. Amrie, S. Kurniawan, J. H. Windiatmaja, dan Y. Ruldeviyani, “Analysis of Google Play Store’s Sentiment Review on Indonesia’s P2P Fintech Platform,” dalam 2022 IEEE Delhi Section Conference (DELCON), IEEE, Feb 2022, hlm. 1–5. doi: 10.1109/DELCON54057.2022.9753108.

R. I. Perwira, V. A. Permadi, D. I. Purnamasari, dan R. P. Agusdin, “Domain-Specific Fine-Tuning of IndoBERT for Aspect-Based Sentiment Analysis in Indonesian Travel User-Generated Content,” Journal of Information Systems Engineering and Business Intelligence, vol. 11, no. 1, hlm. 30–40, Mar 2025, doi: 10.20473/jisebi.11.1.30-40.

K. L. Tan, C. P. Lee, dan K. M. Lim, “A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research,” Applied Sciences, vol. 13, no. 7, hlm. 4550, Apr 2023, doi: 10.3390/app13074550.

A. Palanivinayagam, C. Z. El-Bayeh, dan R. Damaševičius, “Twenty Years of Machine-Learning-Based Text Classification: A Systematic Review,” Algorithms, vol. 16, no. 5, hlm. 236, Apr 2023, doi: 10.3390/a16050236.

A. D. Gustiavani dan M. Muljono, “Sentiment Classification of Indonesian E-Government Application Reviews Using Advanced Learning Models,” Journal of Applied Informatics and Computing, vol. 10, no. 2, hlm. 1662–1673, Apr 2026, doi: 10.30871/jaic.v10i2.12217.

I. G. B. A. Budaya dan I. K. P. Suniantara, “Comparison of Sentiment Analysis Algorithms with SMOTE Oversampling and TF-IDF Implementation on Google Reviews for Public Health Centers,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 3, hlm. 1077–1086, Jul 2024, doi: 10.57152/malcom.v4i3.1459.

A. Vaswani dkk., “Attention Is All You Need,” 2017. Diakses: 7 Juni 2026. [Daring]. Tersedia pada: https://arxiv.org/abs/1706.03762

J. Devlin, M.-W. Chang, K. Lee, dan K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” dalam Proceedings of the 2019 Conference of the North, Stroudsburg, PA, USA: Association for Computational Linguistics, 2019, hlm. 4171–4186. doi: 10.18653/v1/N19-1423.

J. D. Romero, M. A. Feijoo-Garcia, G. Nanda, B. Newell, dan A. J. Magana, “Evaluating the Performance of Topic Modeling Techniques with Human Validation to Support Qualitative Analysis,” Big Data and Cognitive Computing, vol. 8, no. 10, hlm. 132, Okt 2024, doi: 10.3390/bdcc8100132.

A. Bau dan G. D. Kapitan, “Sentiment Analysis of National Health Insurance Participants’ Reviews on Google Reviews,” Jurnal Jaminan Kesehatan Nasional, vol. 5, no. 2, hlm. 365–378, Des 2025, doi: 10.53756/jjkn.v5i2.344.


Refbacks

  • There are currently no refbacks.


Editorial Office :

Prosiding SENDIKO (Seminar Nasional Hasil Penelitian & Pengabdian Masyarakat Bidang Ilmu Komputer)
Published by Universitas PGRI Madiun
Managed by Program Studi Sistem Informasi Fakultas Teknik Universitas PGRI Madiun
Address Jl. Auri 14-16 Kota Madiun Kampus III Universitas PGRI Madiun 63118
Website http://prosiding.unipma.ac.id/index.php/sendiko/index
Email [email protected]

e-ISSN:  3025-4604