LRU-Based Caching Optimization for REST API and GraphQL in a PBB-P2 Information System
DOI:
https://doi.org/10.58344/locus.v5i8.6214Keywords:
Application-Level Caching, GraphQL, LRU Cache, PBB-P2, Performance Testing, REST APIAbstract
The increasing demand for fast and reliable access to taxpayer, tax object, billing, and payment information requires efficient data retrieval mechanisms in PBB-P2 information systems. Repeated database access for frequently requested data can increase server workload and prolong response times. Application-level caching can reduce redundant database operations; however, cache performance depends on the replacement policy applied when memory capacity is limited. This study aimed to evaluate the performance impact of Least Recently Used (LRU) caching compared with simple bounded caching and no-cache conditions in REST API- and GraphQL-based PBB-P2 information systems. A quantitative comparative experimental method was employed using a Node.js prototype connected to a MySQL/MariaDB database. The experiment evaluated three cache conditions (no cache, simple cache, and LRU cache) across four workload patterns: random, repeat, mixed, and pressure. Performance was assessed using response time, throughput, cache hit ratio, database query count, CPU utilization, and eviction accuracy. The results showed that LRU provided the most significant improvement under high cache eviction pressure conditions. In REST API testing, LRU reduced response time from 10.41 ms to 7.89 ms and decreased database queries by 74.66%. In GraphQL testing, LRU reduced response time from 19.58 ms to 17.03 ms and decreased database queries by 74.96%. However, LRU was not consistently superior, particularly when workloads had sufficient cache capacity or irregular access patterns. The study concludes that LRU caching is effective when temporal locality and cache pressure are present; however, its implementation should be guided by workload characteristics and system telemetry.
References
Azmar, M., Junita, A., & Kusmanto, H. (2025). Effectiveness of Tax Object Information Management System in Improving PBB P2 Services. Journal La Sociale, 6(2), 600–609.
Bogner, J., Kotstein, S., & Pfaff, T. (2023). Do RESTful API design rules have an impact on the understandability of Web APIs? Empirical Software Engineering, 28(6), Article 132. https://doi.org/10.1007/s10664-023-10367-y
Brito, G., & Valente, M. T. (2020). REST vs. GraphQL: A controlled experiment. In 2020 IEEE International Conference on Software Architecture (ICSA) (pp. 81–91). IEEE. https://doi.org/10.1109/ICSA47634.2020.00016
Cha, A., Wittern, E., Baudart, G., Davis, J. C., Mandel, L., & Laredo, J. A. (2020). A principled approach to GraphQL query cost analysis. In Proceedings of the 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (pp. 11–22). ACM. https://doi.org/10.1145/3368089.3409670
Chandra, S., & Farisi, A. (2025). Comparative analysis of RESTful, GraphQL, and gRPC APIs: Performance insight from load and stress testing. Jurnal Sisfokom (Sistem Informasi dan Komputer), 14(1), 81–85. https://doi.org/10.32736/sisfokom.v14i1.2315
Darmawan, R. T., & Citrawan, A. L. (2025). Effectiveness of Official Assessment System for PBB-P2 Tax Collection in Bekasi City. Studia: Journal of Humanities and Education Studies, 1(2), 14–27.
Hadinata, W., & Stianingsih, L. (2024). Analisis perbandingan performa RESTful API antara Express.js dengan Laravel Framework. Jurnal Informatika dan Teknik Elektro Terapan, 12(1). https://doi.org/10.23960/jitet.v12i1.3845
Hartina, D. A., Lawi, A., & Panggabean, B. L. E. (2018). Performance analysis of GraphQL and RESTful in SIM LP2M of Hasanuddin University. In Proceedings of the 2nd East Indonesia Conference on Computer and Information Technology (pp. 237–240). IEEE. https://doi.org/10.1109/EIConCIT.2018.8878524
Inoue, H. (2021). Multi-step LRU: SIMD-based cache replacement for lower overhead and higher precision. In 2021 IEEE International Conference on Big Data (pp. 174–180). IEEE. https://doi.org/10.1109/BigData52589.2021.9671363
Lawi, A., Panggabean, B. L. E., & Yoshida, T. (2021). Evaluating GraphQL and REST API services performance in a massive and intensive accessible information system. Computers, 10(11), Article 138. https://doi.org/10.3390/computers10110138
Lee, E., et al. (2020). Performance measurement of GraphQL API in home ESS data server. In Proceedings of the International Conference on Information and Communication Technology Convergence. IEEE. https://doi.org/10.1109/ICTC49870.2020.9289569
Mandiri, P., Alfitri, A., Thamrin, M. H., & Najib, A. (2024). Modernizing local tax policy: the role of digitalization in land and building tax administration. Journal Public Policy, 10(3), 204–213.
Muzaki, R. N., & Salam, A. (2024). Reducing under-fetching and over-fetching in REST API with GraphQL for web-based software development. Jurnal Teknik Informatika (JUTIF), 5(2), 447–453. https://doi.org/10.52436/1.jutif.2024.5.2.1725
Neumann, A., Laranjeiro, N., & Bernardino, J. (2021). An analysis of public REST web service APIs. IEEE Transactions on Services Computing, 14(4), 957–970. https://doi.org/10.1109/TSC.2018.2847344
Nottingham, M. (2022). HTTP caching (RFC 9111). Internet Engineering Task Force. https://doi.org/10.17487/RFC9111
Prabowo, B. E., & Tambunan, M. R. U. D. (2024). Factors Influencing Taxpayer Compliance In The Implementation Of The Land And Building Tax (PBB-P2) Policy In The Revenue Office Districts Of North Jakarta Administrative City And The Kepulauan Seribu Regency. Eduvest-Journal of Universal Studies, 4(4), 1756–1766.
Quiña-Mera, A., Fernandez, P., García, J. M., & Ruiz-Cortés, A. (2023). GraphQL: A systematic mapping study. ACM Computing Surveys, 55(10), Article 202. https://doi.org/10.1145/3561818
Rahmawati, R., & Putra, R. (2024). Performance of The Public Service Bureaucracy: Urban, Rural, and Building Tax Service Model (PBB-P2) Based on Information and Technology. BISNIS & BIROKRASI: Jurnal Ilmu Administrasi Dan Organisasi, 31(2), 64–73.
Sinaga, R., Samsinar, S., Fatima, S., & Frangky, F. (2025). Analysis of security challenges in REST API in edge computing-based IoT ecosystem: A review. JIKO (Jurnal Informatika dan Komputer), 8(2), 153–160. https://doi.org/10.33387/jiko.v8i2.10097
?liwa, M., & Pa?czyk, B. (2021). Performance comparison of programming interfaces on the example of REST API, GraphQL and gRPC. Journal of Computer Sciences Institute, 21, 356–361. https://doi.org/10.35784/jcsi.2744
Wang, Y., Yang, J., & Wang, Z. (2020). Dynamically configuring LRU replacement policy in Redis. In Proceedings of the International Symposium on Memory Systems (MEMSYS) (pp. 272–280). ACM. https://doi.org/10.1145/3422575.3422799
Yang, J., Yue, Y., & Rashmi, K. V. (2021). A large-scale analysis of hundreds of in-memory key-value cache clusters at Twitter. ACM Transactions on Storage, 17(3), 1–35. https://doi.org/10.1145/3468521
Yusuf, O. S., Sholahuddin, A., & Suhartono, T. (2024). Absorption of Rural and Urban Land and Building Tax (PBB-P2) Through the Tax Object Management Information System: Study of Implementation of Tax Object Management Information System Policy Based on East Kutai Regent Regulation Number 44 of 2013 in the East Kutai Regency Area. International Journal of Research in Social Science and Humanities (IJRSS) ISSN: 2582-6220, DOI: 10.47505/IJRSS, 5(2), 49–63.
Zulfa, M. I., Fadli, A., Permanasari, A. E., & Ahmed, W. A. (2023). Performance comparison of cache replacement algorithms on various internet traffic. Jurnal INFOTEL, 15(1), 1–7. https://doi.org/10.20895/infotel.v15i1.872
Zulfa, M. I., Maryani, S., Ardiansyah, T., Widiyaningtyas, T., & Ali, W. (2024). Application-level caching approach based on enhanced aging factor and Pearson correlation coefficient. JOIV: International Journal of Informatics Visualization, 8(1), 31–37. https://doi.org/10.62527/joiv.8.1.2143
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Rudi Setiawan Samosir, David JM Sembiring , Roberto Kaban
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-ShareAlike 4.0 International (CC-BY-SA). 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.
