Application of Fuzzy Search Method with Postgresql Trigram for Information Search Optimization on the Bojonegoro Regency PPID Website

Authors

  • Abdulloh Rozi Universitas Hasyim Asy'ari
  • Aries Dwi Indriyanti Universitas Hasyim Asy'ari

Keywords:

Fuzzy Search, PostgreSQL Trigram, Similarity Threshold, Information Retrieval, KIP, PID Bojonegoro

Abstract

The successful implementation of Law Number 14 of 2008 concerning Public Information Disclosure (Keterbukaan Informasi Publik - KIP) fundamentally relies on the public's ability to easily access information. The Official Information and Documentation Management Officer (Pejabat Pengelola Informasi dan Dokumentasi - PPID) Website of Bojonegoro Regency serves as the primary gateway for this service. However, the conventional search system (exact match) previously implemented proved to be rigid, often failing to retrieve documents due to minor typing errors (typos), thereby diminishing service efficiency. This research aims to design and implement a Fuzzy Search mechanism utilizing the PostgreSQL Trigram extension to replace the old system and to quantitatively evaluate its effectiveness. The Waterfall model was employed as the system development methodology, with implementation focusing on the backend using the PHP framework and a PostgreSQL database. A comparative experiment was conducted, contrasting the proposed system's performance against the existing exact match system , measured using standard Information Retrieval (IR) metrics: Precision, Recall, and F-Measure. The findings demonstrate that the existing exact match system showed a 100% failure rate when faced with common typing errors, whereas the Fuzzy Search implementation significantly increased error tolerance. Through empirical comparison, a Similarity Threshold configuration of 0.2 for PostgreSQL Trigram was identified as the optimal setting for the PPID Bojonegoro dataset. This value yielded the highest F-Measure score of 88.5%, reflecting the best balance between Recall (89%) and Precision (88%). Furthermore, the application of a GIN Index on PostgreSQL dramatically optimized performance, accelerating the search process by up to 40 times compared to the unindexed method. The proposed system successfully addresses the weakness against typos and ensures fast, inclusive public access to information, which is a key requirement under the KIP Law.

References

Airbyte, “PostgreSQL vs MySQL: A Detailed Comparison for Data Engineers,” 2025. [Online]. Available: https://airbyte.com/data-engineering-resources/postgresql-vs-mysql

R. Baeza-Yates and B. Ribeiro-Neto, Modern Information Retrieval: The Concepts and Technol-ogy behind Search, 2nd ed. Boston, MA, USA: Addison-Wesley Professional, 2011.

T. Connolly and C. Begg, Database Systems: A Practical Approach to Design, Implementation, and Management, 6th ed. Harlow, UK: Pearson Education, 2015.

Dennenboom Blog, “The Hidden Superpowers of PostgreSQL: Fuzzy Search,” 2025. [Online]. Available: https://dennenboom.be/blog/the-hidden-superpowers-of-postgresql-fuzzy-search

Digoal, “PostgreSQL Fuzzy Search Best Practices: Single-word, Double-word, and Multi-word Fuzzy Search Methods,” Alibaba Cloud Community, 2017. [Online]. Available: https://www.alibabacloud.com/blog/postgresql-fuzzy-search-best-practices

A. Effendri and H. Ma’sum, “Perancangan Aplikasi Aset Manajemen Menggunakan Framework Laravel di PT Dirgantara Indonesia (IAe),” Jurnal Informatika dan Teknik Elektro Terapan, vol. 13, no. 2, 2025. [Online]. Available: https://journal.eng.unila.ac.id/index.php/jitet/article/view/6405

K. E. Kendall and J. E. Kendall, Analisis dan Perancangan Sistem, 5th ed. Jakarta, Indonesia: In-deks, 2010.

M. A. A. Murad et al., “Database Performance Analysis in MySQL, PostgreSQL, and SQL Server,” International Journal of Computer Science Issues (IJCSI), vol. 10, no. 5, no. 2, 2013.

S. Nidhra and J. Dondeti, “Black Box and White Box Testing Techniques – A Literature Review,” International Journal of Embedded Systems and Applications (IJESA), vol. 2, no. 2, pp. 29–50, 2012.

PostgreSQL Global Development Group, “F.35. pg_trgm Support for similarity of text using trigram matching,” PostgreSQL Documentation, 2025. [Online]. Available: https://www.postgresql.org/docs/current/pgtrgm.html

R. S. Pressman, Software Engineering: A Practitioner’s Approach, 8th ed. New York, NY, USA: McGraw-Hill Education, 2015.

“Performance analysis of relational databases MySQL, PostgreSQL and Oracle using Doctrine libraries,” Journal of Computer Sciences Institute, no. 24, pp. 250–257, 2022. [Online]. Available: https://www.researchgate.net/publication/364068579

S. V. Salunke and A. Ouda, “A performance benchmark for the PostgreSQL and MySQL data-bases,” Future Internet, vol. 16, no. 10, p. 382, 2024, doi: 10.3390/fi16100382.

U. Matyáš, “PostgreSQL Query Optimization Strategies,” Bachelor thesis, Czech Technical Uni-versity in Prague, 2025. [Online]. Available: https://dspace.cvut.cz/bitstream/handle/10467/122700/F3-BP-2025-Urban-Matyas-thesis.pdf

R. K. Wadhwa and K. A. G. Perera, “A comparative study of Laravel and Symfony PHP frame-works,” 2021. [Online]. Available: https://www.researchgate.net/publication/330656221

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Published

2026-03-12

How to Cite

Abdulloh Rozi, & Aries Dwi Indriyanti. (2026). Application of Fuzzy Search Method with Postgresql Trigram for Information Search Optimization on the Bojonegoro Regency PPID Website. Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID), 5(03), 558–571. Retrieved from https://ejournal.seaninstitute.or.id/index.php/esaprom/article/view/7889