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Fidela Carissa Aramintha
Universitas Pembangunan Nasional "Veteran" Jawa Timur

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Design and Implementation of Digital Medical Record System Using the Boyer–Moore Algorithm Fidela Carissa Aramintha; Afina Lina Nurlaili; Firza Prima Aditiawan
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2959

Abstract

Praktik Mandiri Bidan (PMB) Siti Nur Aini, A.Md.Keb., a primary healthcare facility specializing in maternal and child health, continues to rely on manual, paper-based medical records. This practice causes slow data retrieval, high risk of data loss, and storage limitations. To address these issues, this study developed BIDIG (Bidan Medis Digital), a web-based medical record system integrating the Boyer–Moore string matching algorithm to accelerate and enhance the accuracy of patient record searches. The Boyer–Moore algorithm was selected for its proven efficiency in pattern matching, enabling larger shift steps during mismatches and outperforming other methods such as Rabin–Karp, Brute Force, and Knuth–Morris–Pratt in prior research. BIDIG was implemented using the Laravel framework and MySQL database, with features for patient registration, medical record management, and reporting. Usability testing with the System Usability Scale (SUS) involved 10 respondents and achieved an average score of 87.25 (category: Acceptable, grade B, adjective rating: Excellent). Comparative performance evaluation using a Confusion Matrix on 160 records yielded accuracy of 0.9977, precision of 0.9318, and recall of 1.00, confirming near-perfect retrieval capability with minimal false positives. These results demonstrate that integrating an efficient search algorithm with an intuitive interface can substantially improve service quality and operational efficiency in small-scale healthcare facilities. The findings underscore the potential of algorithm-driven digitalization to overcome resource constraints, reduce errors, and accelerate access to critical health information offering a scalable model for broader adoption in similar clinical settings.