Unraveling Insights from User Reviews of Sapawarga – Jabar Super Apps Through Topic Modeling
DOI:
https://doi.org/10.30871/jaic.v10i4.12624Keywords:
Application, LDA, Sapawarga, Topic Modeling, User reviewAbstract
This study conducted topic modeling on user reviews of the Sapawarga – Jabar Super App application on the Google Play Store. The dataset comprised user reviews submitted since the date application released until January 7, 2026 yielded 5.082 user reviews. Collected data processed through a series of data processing pipeline namely case folding, remove punctuation, normalization, tokenization, stop word removal and stemming. Topic modeling was conducted using Latent Dirichlet Allocation (LDA) algorithm grouped based on year of data. Topic distribution was visualized using the PyLDAvis to facilitate analysis and interpretation. This study found there are 9 topic after analysing year by year data namely Appreciation of the usefulness of the application in helping residents (1), Technical issues in terms of how to log in to the application (2), Application improvement proposal (3), Ease of information and citizen empowerment (4), General expressions of appreciation and criticism from citizens towards the application (5), Technical issues with payment features (6), Application error problem (7), User appreciation for the ease of vehicle tax payments from the application (8) and Criticism of application constraints in vehicle tax payments (9). The interpretation results indicate that the application is widely used by residents for online tax payments, particularly vehicle tax services, compared to other features or uses of the application. The findings of this study are expected to serve as a valuable insight for relevant stakeholders to improve the quality, performance, and overall effectiveness of the application as a public service platform.
Downloads
References
[1] Latip, D. Mirza, N. Lastri, and V. Aditiya, “Optimizing Digital Public Services for Enhanced Citizen Satisfaction and Transparent Governance,” Jurnal Ilmiah Manajemen Kesatuan, vol. 13, no. 6, pp. 4563–4574, Nov. 2025, doi: 10.37641/jimkes.v13i6.4100.
[2] A. Sandhika and S. Novani, “Developing Sapawarga As Value Orchestration Platform Using Service Science Perspective (Case Study Of West Java Provincial Government),” Journal of Social Research, vol. 4, no. 7, pp. 1516–1534, Jun. 2025, doi: 10.55324/josr.v4i7.2603.
[3] M. Fauzi, Y. S. Sugandi, D. Hadi Kushendar, S. Tinggi, I. A. Bandung, and U. Padjajaran, “The Citizen Centric Approach In The Implementation Of E-Government As An Effort To Improve The Quality Of Public Services In The Government Of West Java Province,” Jurnal Ekonomi, vol. 12, p. 2023, 2023, [Online]. Available: http://ejournal.seaninstitute.or.id/index.php/Ekonomi
[4] R. Churchill and L. Singh, “The Evolution of Topic Modeling,” ACM Comput. Surv., vol. 54, no. 10, Jan. 2022, doi: 10.1145/3507900.
[5] A. Alotaibi and N. Altwaijry, “A Comparison of Topic Modeling Algorithms on Visual Social Media Networks,” in Proceedings of 2022 2nd International Conference on Computing and Information Technology, ICCIT 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 26–31. doi: 10.1109/ICCIT52419.2022.9711617.
[6] D. Yu and B. Xiang, “Discovering topics and trends in the field of Artificial Intelligence: Using LDA topic modeling,” Expert Syst. Appl., vol. 225, Sep. 2023, doi: 10.1016/j.eswa.2023.120114.
[7] C. Indah Ratnasari, “Topic Modeling of Skincare Comments from Female Daily,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[8] Uray Nur Khadijah and Nuri Cahyono, “Analisis Topic Modelling Pariwisata Yogyakarta Menggunakan Latent Dirichlet Allocation (LDA),” The Indonesian Journal of Computer Science, vol. 13, no. 4, Jul. 2024, doi: 10.33022/ijcs.v13i4.3816.
[9] W. A. Nawang Sari and H. Dwi Purnomo, “Topic Modeling Using The Latent Dirichlet Allocation Method On Wikipedia Pandemic Covid-19 Data In Indonesia,” Jurnal Teknik Informatika (Jutif), vol. 3, no. 5, pp. 1223–1230, Oct. 2022, doi: 10.20884/1.jutif.2022.3.5.321.
[10] Y. Pamungkas, “Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 13, no. 2, pp. 237–244, Jun. 2024, doi: 10.32736/sisfokom.v13i2.2117.
[11] M. Detricia Pratiwi and K. Ditha Tania, “Knowledge Discovery Through Topic Modeling on GoPartner User Reviews Using BERTopic, LDA, and NMF,” 2025. [Online]. Available: http://jurnal.polibatam.ac.id/index.php/JAIC
[12] R. Nadzira, Z. Shafa, and A. Herliana, “Analisis Sentimen Pengguna Aplikasi Sapawarga Jabar Super Apps Menggunakan Algoritma Support Vector Machine,” Jurnal Teknologi Informasi, vol. 6, no. 2, 2025, doi: 10.46576/djtechno.
[13] F. T. Sanudin, B. Irawan, and A. Bahtiar, “Analisis Sentimen Ulasan Aplikasi Sapawarga Di Playstore Menggunkan Algoritma Naïve Bayes,” 2024.
[14] S. Mutmainah, D. H. Fudholi, and S. Hidayat, “Analisis Sentimen dan Pemodelan Topik Aplikasi Telemedicine Pada Google Play Menggunakan BiLSTM dan LDA,” Jurnal Media Informatika Budidarma, vol. 7, no. 1, p. 312, Jan. 2023, doi: 10.30865/mib.v7i1.5486.
[15] A. Tawakuli and T. Engel, “Towards Normalizing The Design Phase of Data Preprocessing Pipelines For IoT Data,” in Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 4589–4594. doi: 10.1109/BigData55660.2022.10020312.
[16] A. Abdelrazek, Y. Eid, E. Gawish, W. Medhat, and A. Hassan, “Topic modeling algorithms and applications: A survey,” Feb. 01, 2023, Elsevier Ltd. doi: 10.1016/j.is.2022.102131.
[17] A. D. Arko, M. Yusril, H. Setyawan, and R. Andarsyah, “Topic Modeling for Constructing Learning Profiles using Latent Dirichlet Allocation and Coherence Evaluation,” JUTI: Jurnal Ilmiah Teknologi Informasi, vol. 23, no. 2, pp. 126–143, 2025, doi: 10.12962/j24068535.v23i1.a1301.
[18] B. F. S. Supriyanto and S. Rosalin, “Analisis Sentimen Program Merdeka Belajar dengan Text Analysis Wordcloud & Word Frequency,” Jurnal Minfo Polgan, vol. 12, no. 1, pp. 25–32, Mar. 2023, doi: 10.33395/jmp.v12i1.12312.
[19] D. Nur, G. Kamil, D. Mulyana, E. Ariadne, and S. Dewi, “Persepsi Pengguna Terhadap Inovasi Pelayanan Publik Pemerintah Provinsi Jawa Barat Melalui Aplikasi Sapawarga,” 2025.
[20] R. C. Nariti, “Volume, 2 Nomor 2 Desember 2025 Pemanfaatan Platform Digital Sapawarga dalam Meningkatkan Pelayanan Publik di Provinsi Jawa Barat Article info Abstrak,” 2023.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Basrah Nasution, Indra Budi, Aris Budi Santoso, Prabu Kresna Putra
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
